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2015 Vol. 37, No. 4
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2015, 37(4): 763-769.
doi: 10.11999/JEIT140875
Abstract:
Nowadays the deterministic construction of sensing matrices is a hot topic in compressed sensing. Two classes of deterministic sensing matrices based on the Berlekamp-Justesen (B-J) codes are proposed. Firstly, a class of sparse sensing matrices with near-optimal coherence is constructed. It satisfies the Restricted Isometry Property (RIP) well. Afterwards, a class of deterministic complex-valued matrices is proposed. The row and column numbers of these matrices are tunable through the row and column puncturing. Moreover, the first proposed matrices are high sparsity and the second matrices are able to obtain from the cyclic matrices, thus the storage costs of them are relatively low and both the sampling and recovery processes can be simpler. The simulation results demonstrate that the proposed matrices often perform comparably to, or even better than some random matrices and deterministic measurement matrices.
Nowadays the deterministic construction of sensing matrices is a hot topic in compressed sensing. Two classes of deterministic sensing matrices based on the Berlekamp-Justesen (B-J) codes are proposed. Firstly, a class of sparse sensing matrices with near-optimal coherence is constructed. It satisfies the Restricted Isometry Property (RIP) well. Afterwards, a class of deterministic complex-valued matrices is proposed. The row and column numbers of these matrices are tunable through the row and column puncturing. Moreover, the first proposed matrices are high sparsity and the second matrices are able to obtain from the cyclic matrices, thus the storage costs of them are relatively low and both the sampling and recovery processes can be simpler. The simulation results demonstrate that the proposed matrices often perform comparably to, or even better than some random matrices and deterministic measurement matrices.
2015, 37(4): 770-776.
doi: 10.11999/JEIT140949
Abstract:
Image restoration is a long-standing and challenging inverse issue. In order to recover an image from its blurry version blindly, a multi-regularization hybrid constraints method is proposed. First, the large scale edges are extracted from the image with a Local Structure Extraction Scheme (LSES). Then, in the Blur Kernel (BK) estimation step, the extracted large scale edges are used for BK estimation, and a sparsity and smoothness dual-regularization constraints model proposed in the previous study, is also employed for estimating BK more accurately. In the image restoration step, a multi-regularization constraints model, which combines the Total Variation (TV) model and Shock filtering invariance, is proposed for obtaining high-quality restoration image. Finally, in order to exactly estimate the BK and simultaneously obtain high-quality restoration image, the proposed models are addressed with a half-quadratic variables splitting scheme. A large number of experiments are performed on both synthetic blurred images and real-life blurred images. The experimental results demonstrate the effectiveness of the proposed method, while in comparison with several recent representative image blind restoration methods, not only the subjective vision, but also the objective numerical measurement has obvious improvement.
Image restoration is a long-standing and challenging inverse issue. In order to recover an image from its blurry version blindly, a multi-regularization hybrid constraints method is proposed. First, the large scale edges are extracted from the image with a Local Structure Extraction Scheme (LSES). Then, in the Blur Kernel (BK) estimation step, the extracted large scale edges are used for BK estimation, and a sparsity and smoothness dual-regularization constraints model proposed in the previous study, is also employed for estimating BK more accurately. In the image restoration step, a multi-regularization constraints model, which combines the Total Variation (TV) model and Shock filtering invariance, is proposed for obtaining high-quality restoration image. Finally, in order to exactly estimate the BK and simultaneously obtain high-quality restoration image, the proposed models are addressed with a half-quadratic variables splitting scheme. A large number of experiments are performed on both synthetic blurred images and real-life blurred images. The experimental results demonstrate the effectiveness of the proposed method, while in comparison with several recent representative image blind restoration methods, not only the subjective vision, but also the objective numerical measurement has obvious improvement.
2015, 37(4): 777-783.
doi: 10.11999/JEIT140739
Abstract:
In neighbor embedding based face hallucination, the training and reconstruction processes are performed in the feature space, thus the feature selection is important. In addition, there is no constraint specified for the signs of the weights generated in neighbor embedding algorithm, which leads to over-fitting and degradation of the recovered face images. Considering the importance of feature selection and the constraints of weights, a novel neighbor embedding face hallucination method is proposed based on non-negative weights and Two-Dimensional Principal Component Analysis (2D-PCA) features. First, the face images are partitioned into patches, and the local visual primitives are obtained by k-means clustering algorithm. The face image patches are classified with the local visual primitives generated before. Second, the feature of face image patches is captured with 2D-PCA, and the low and high dictionary is established. Finally, a novel non-negative weights solution method is used to obtain the weights. The experiment results show that the weights computed by the proposed method have more stable behavior and obviously less over-fitting phenomenon, furthermore, the recovery face images have better subjective and objective quality.
In neighbor embedding based face hallucination, the training and reconstruction processes are performed in the feature space, thus the feature selection is important. In addition, there is no constraint specified for the signs of the weights generated in neighbor embedding algorithm, which leads to over-fitting and degradation of the recovered face images. Considering the importance of feature selection and the constraints of weights, a novel neighbor embedding face hallucination method is proposed based on non-negative weights and Two-Dimensional Principal Component Analysis (2D-PCA) features. First, the face images are partitioned into patches, and the local visual primitives are obtained by k-means clustering algorithm. The face image patches are classified with the local visual primitives generated before. Second, the feature of face image patches is captured with 2D-PCA, and the low and high dictionary is established. Finally, a novel non-negative weights solution method is used to obtain the weights. The experiment results show that the weights computed by the proposed method have more stable behavior and obviously less over-fitting phenomenon, furthermore, the recovery face images have better subjective and objective quality.
2015, 37(4): 784-790.
doi: 10.11999/JEIT141282
Abstract:
Extracting an effective visual feature to uncover semantic information is an important work for designing a robust automatic image annotation system. Since different kinds of heterogeneous features (such as color, texture and shape) show different intrinsic discriminative power and the same kind of features are usually correlated for image understanding, a Graph Regularized Non-negative Group Sparsity (GRNGS) model for image annotation is proposed, which can be effectively solved by a new method of non-negative matrix factorization. This model combines graph regularization withl2,1-norm regularization, and is able to select proper group features, which can describe both visual similarities and semantic correlations when performing the task of image annotation. Experimental results reported over the Corel5K and ESP Game databases show the robust capability and good performance of the proposed method.
Extracting an effective visual feature to uncover semantic information is an important work for designing a robust automatic image annotation system. Since different kinds of heterogeneous features (such as color, texture and shape) show different intrinsic discriminative power and the same kind of features are usually correlated for image understanding, a Graph Regularized Non-negative Group Sparsity (GRNGS) model for image annotation is proposed, which can be effectively solved by a new method of non-negative matrix factorization. This model combines graph regularization withl2,1-norm regularization, and is able to select proper group features, which can describe both visual similarities and semantic correlations when performing the task of image annotation. Experimental results reported over the Corel5K and ESP Game databases show the robust capability and good performance of the proposed method.
2015, 37(4): 791-797.
doi: 10.11999/JEIT140743
Abstract:
Considering the ambiguity problem in the traditional feature coding model, a feature context-aware semantic enhanced linear coding method is proposed. At first, the context information is represented by the concurrence matrix learnt from local area of the features. Then, the context information and a context matching weight are introduced into the coding model to form a new semantic enhanced coding model. Owning to the functions of context information and the context matching weight, this model not only enriches the semantic meaning of coding, but also efficiently avoids the affects of noise. Experiments on the baselines (Scene15, Caltech101, and Caltech256) demonstrate the effectiveness of the proposed method.
Considering the ambiguity problem in the traditional feature coding model, a feature context-aware semantic enhanced linear coding method is proposed. At first, the context information is represented by the concurrence matrix learnt from local area of the features. Then, the context information and a context matching weight are introduced into the coding model to form a new semantic enhanced coding model. Owning to the functions of context information and the context matching weight, this model not only enriches the semantic meaning of coding, but also efficiently avoids the affects of noise. Experiments on the baselines (Scene15, Caltech101, and Caltech256) demonstrate the effectiveness of the proposed method.
2015, 37(4): 798-803.
doi: 10.11999/JEIT140560
Abstract:
To pursue accurate source location and velocity, this paper proposes a method based on the Regularization theory to solve the source localization problem utilizing Time-Difference-Of-Arrival (TDOA) and Frequency-Difference-Of-Arrival (FDOA). The proposed algorithm determines the objective function using the maximum likelihood estimator, and then uses classical Newton method to estimate the source position and velocity in an iterative way. It is known that the Newton method requires a good initial value, and a bad initial value can cause an ill-posed Hess matrix which leads to the iteration divergence. This paper introduces the Regularization theory to modify the Hess matrix to make it more proper, which ensures the iteration convergence. The experiment results show that compared with the classical Newton method, the proposed algorithm is robust to the initial value, and is still able to ensure its convergence even with an inaccurate initial value of large error. Compared with some other closed-form source location methods, the proposed algorithm has better location accuracy in large noise levels which can achieve the Cramer-Rao bound. The proposed algorithm can be widely applied in practice.
To pursue accurate source location and velocity, this paper proposes a method based on the Regularization theory to solve the source localization problem utilizing Time-Difference-Of-Arrival (TDOA) and Frequency-Difference-Of-Arrival (FDOA). The proposed algorithm determines the objective function using the maximum likelihood estimator, and then uses classical Newton method to estimate the source position and velocity in an iterative way. It is known that the Newton method requires a good initial value, and a bad initial value can cause an ill-posed Hess matrix which leads to the iteration divergence. This paper introduces the Regularization theory to modify the Hess matrix to make it more proper, which ensures the iteration convergence. The experiment results show that compared with the classical Newton method, the proposed algorithm is robust to the initial value, and is still able to ensure its convergence even with an inaccurate initial value of large error. Compared with some other closed-form source location methods, the proposed algorithm has better location accuracy in large noise levels which can achieve the Cramer-Rao bound. The proposed algorithm can be widely applied in practice.
2015, 37(4): 804-810.
doi: 10.11999/JEIT140511
Abstract:
Due to the huge computation for eigenvalue decomposition based signal synthesis method, an efficient multi-component signals reconstruction algorithm is presented in this paper. Firstly, by analyzing the inverse transformation for Wigner-Ville distribution, a fast signal reconstruction is developed using the inherent relationship between original signal and synthesized signal. Then, the smoothed pseudo Wigner-Ville distribution is used as a time-frequency masking to suppress the cross-terms, and the sliding window method in time-frequency plane is adopted to extract signals one by one. Finally, by combining the signal synthesis algorithm and the sliding window masking method, multi-component signals reconstruction can be realized efficiently and accurately. Simulation results demonstrate the effectiveness and feasibility of the proposed algorithm.
Due to the huge computation for eigenvalue decomposition based signal synthesis method, an efficient multi-component signals reconstruction algorithm is presented in this paper. Firstly, by analyzing the inverse transformation for Wigner-Ville distribution, a fast signal reconstruction is developed using the inherent relationship between original signal and synthesized signal. Then, the smoothed pseudo Wigner-Ville distribution is used as a time-frequency masking to suppress the cross-terms, and the sliding window method in time-frequency plane is adopted to extract signals one by one. Finally, by combining the signal synthesis algorithm and the sliding window masking method, multi-component signals reconstruction can be realized efficiently and accurately. Simulation results demonstrate the effectiveness and feasibility of the proposed algorithm.
2015, 37(4): 811-816.
doi: 10.11999/JEIT141034
Abstract:
Subspace based Direction-Of-Arrival (DOA) estimators require usually the number of sources to be known in advance. If the number of sources is incorrectly estimated, the performance of those methods is able to deteriorate significantly. This paper presents a novel efficient Semi-Real-Valued Capon (SRV-Capon) algorithm for DOA estimation with unknown number of signals. Compared with state-of-the-art real-valued techniques suitable for only Centro-Symmetrical Arrays (CSAs), the proposed method can be used with arbitrary arrays. Unlike conventional Capon with heavy complex computations, SRV-Capon exploits only the real part of the array output covariance matrix, leading to a real-valued spectral search over only half of the total angular field-of-view, which hence reduces about 75% computational complexity. Theoretical analysis and simulations demonstrate the effectiveness of the proposed approach.
Subspace based Direction-Of-Arrival (DOA) estimators require usually the number of sources to be known in advance. If the number of sources is incorrectly estimated, the performance of those methods is able to deteriorate significantly. This paper presents a novel efficient Semi-Real-Valued Capon (SRV-Capon) algorithm for DOA estimation with unknown number of signals. Compared with state-of-the-art real-valued techniques suitable for only Centro-Symmetrical Arrays (CSAs), the proposed method can be used with arbitrary arrays. Unlike conventional Capon with heavy complex computations, SRV-Capon exploits only the real part of the array output covariance matrix, leading to a real-valued spectral search over only half of the total angular field-of-view, which hence reduces about 75% computational complexity. Theoretical analysis and simulations demonstrate the effectiveness of the proposed approach.
2015, 37(4): 817-823.
doi: 10.11999/JEIT140937
Abstract:
Based on the multi-task Bayes Compressive Sensing (BCS), a Direction-Of-Arrival (DOA) estimation strategy using Laplace prior is proposed. The DOA estimation is formulated as the reconstruction of sparse signal constrained by the Laplace prior through the BCS framework. The outputs of array sensors are directly employed as the observations, and the exploiting of Laplace prior leads to better spare property than the conventional BCS method. The proposed method needs not the prior information of the number of sources, needs not the eigenvalue decomposition and can work in the coherent signal scenario. The numerical experiments show that the proposed method has the better performance than the conventional BCS and MUSIC algorithm on the DOA estimation.
Based on the multi-task Bayes Compressive Sensing (BCS), a Direction-Of-Arrival (DOA) estimation strategy using Laplace prior is proposed. The DOA estimation is formulated as the reconstruction of sparse signal constrained by the Laplace prior through the BCS framework. The outputs of array sensors are directly employed as the observations, and the exploiting of Laplace prior leads to better spare property than the conventional BCS method. The proposed method needs not the prior information of the number of sources, needs not the eigenvalue decomposition and can work in the coherent signal scenario. The numerical experiments show that the proposed method has the better performance than the conventional BCS and MUSIC algorithm on the DOA estimation.
2015, 37(4): 824-829.
doi: 10.11999/JEIT140903
Abstract:
In order to recognize automatically the compression and ventilation waveforms of the TransThoracic Impedance (TTI) signal, and obtain the important parameters, for evaluating the CardioPulmonary Resuscitation (CPR) quality, this paper proposes an automatic detection algorithm for TTI signal based on density weighting and preference information. The TTI signals that come from the pig model based on electrically induced cardiac arrest are preprocessed, and the potential compression and ventilation waveforms are marked by using the searching algorithm of multiresolution window after the pretreatment. After that, the width, amplitude and the difference between the adjacent waveforms of the marked waveforms are selected as the features and the signal is divided into several sections according to the width of marked waveforms. Then the original signal is decomposed by wavelet transform. The ratio of the power of each section to the amplitude of the original one is taken as one feature. Finally, k-means clustering algorithm based on density weighting and preference information is used to recognize and classify the compression and ventilation of the marked waveforms. The experimental results show the accuracy and sensitivity of the recognition are high, the robustness is good and the running time (0.430.07 s) can meet the requirement of clinical application.
In order to recognize automatically the compression and ventilation waveforms of the TransThoracic Impedance (TTI) signal, and obtain the important parameters, for evaluating the CardioPulmonary Resuscitation (CPR) quality, this paper proposes an automatic detection algorithm for TTI signal based on density weighting and preference information. The TTI signals that come from the pig model based on electrically induced cardiac arrest are preprocessed, and the potential compression and ventilation waveforms are marked by using the searching algorithm of multiresolution window after the pretreatment. After that, the width, amplitude and the difference between the adjacent waveforms of the marked waveforms are selected as the features and the signal is divided into several sections according to the width of marked waveforms. Then the original signal is decomposed by wavelet transform. The ratio of the power of each section to the amplitude of the original one is taken as one feature. Finally, k-means clustering algorithm based on density weighting and preference information is used to recognize and classify the compression and ventilation of the marked waveforms. The experimental results show the accuracy and sensitivity of the recognition are high, the robustness is good and the running time (0.430.07 s) can meet the requirement of clinical application.
2015, 37(4): 830-835.
doi: 10.11999/JEIT140893
Abstract:
Effective rejection of the time-varying clutter originating from slowly moving vessels and surrounding tissues is very important for depicting hemodynamics in ultrasound color Doppler imaging. In this paper, a new adaptive clutter rejection method based on Hankel Singular Value Decomposition (Hankel-SVD) is presented for suppressing non-stationary clutter. In the proposed method, a Hankel data matrix is created for each slow-time ensemble. Then the orthogonal principle Hankel components can be obtained through the SVD of the Hankel data matrix. It achieves non-stationary clutter suppression by reconstructing the flow signal with only the high order principle Hankel components, which are estimated from the frequency content carried by the principle Hankel components. To assess its efficiency, the proposed Hankel-SVD based method is applied to synthetic slow-time data obtained from a Doppler flow model and carotid arterial complex baseband data acquired by a commercial ultrasound system (Sonix RP). The resulting flow and power images show that the proposed method outperforms the traditional IIR and polynomials regression filter in attenuation of high intense non-stationary clutter signal. It is also adaptive to highly spatially-varying tissue motion and can automatically select the order of the filter, which leads to improved distinguishing between blood and tissue regions compared to other eigen-based filters.
Effective rejection of the time-varying clutter originating from slowly moving vessels and surrounding tissues is very important for depicting hemodynamics in ultrasound color Doppler imaging. In this paper, a new adaptive clutter rejection method based on Hankel Singular Value Decomposition (Hankel-SVD) is presented for suppressing non-stationary clutter. In the proposed method, a Hankel data matrix is created for each slow-time ensemble. Then the orthogonal principle Hankel components can be obtained through the SVD of the Hankel data matrix. It achieves non-stationary clutter suppression by reconstructing the flow signal with only the high order principle Hankel components, which are estimated from the frequency content carried by the principle Hankel components. To assess its efficiency, the proposed Hankel-SVD based method is applied to synthetic slow-time data obtained from a Doppler flow model and carotid arterial complex baseband data acquired by a commercial ultrasound system (Sonix RP). The resulting flow and power images show that the proposed method outperforms the traditional IIR and polynomials regression filter in attenuation of high intense non-stationary clutter signal. It is also adaptive to highly spatially-varying tissue motion and can automatically select the order of the filter, which leads to improved distinguishing between blood and tissue regions compared to other eigen-based filters.
2015, 37(4): 836-840.
doi: 10.11999/JEIT140908
Abstract:
Differential Frequency Hopping (DFH) signal can be used in the High Frequency (HF) and underwater acoustic communications because it has good resistance to multipath fading and jamming, at the same time with a high-speed data transmission capacity. However, taking into account the coding gain is limited within a finite transmission bandwidth, especially multiple Bit Per Hop (BPH), a Compressed Spectrum DFH (CS-DFH) method is proposed to increase the coding gain of DFH signal for the same BPH and bandwidth. This paper develops an upper bound on the Bit Error Rate (BER) of CS-DFH signal in Rician channel, and its tightness is proved by simulations. For the same BPH and bandwidth, the results indicate that the CS-DFH is an efficient mean for obtaining higher coding gain than the existing DFH over the Rician channel, especially when the Rician factor K is small.
Differential Frequency Hopping (DFH) signal can be used in the High Frequency (HF) and underwater acoustic communications because it has good resistance to multipath fading and jamming, at the same time with a high-speed data transmission capacity. However, taking into account the coding gain is limited within a finite transmission bandwidth, especially multiple Bit Per Hop (BPH), a Compressed Spectrum DFH (CS-DFH) method is proposed to increase the coding gain of DFH signal for the same BPH and bandwidth. This paper develops an upper bound on the Bit Error Rate (BER) of CS-DFH signal in Rician channel, and its tightness is proved by simulations. For the same BPH and bandwidth, the results indicate that the CS-DFH is an efficient mean for obtaining higher coding gain than the existing DFH over the Rician channel, especially when the Rician factor K is small.
RF Stealth Design Method for Hopping Cycle and Hopping Interval Based on Conditional Maximum Entropy
2015, 37(4): 841-847.
doi: 10.11999/JEIT140892
Abstract:
In order to increase the interception performance of frequency hopping systems, an optimal algorithm for hopping cycle and hopping interval, which is based on the conditional maximum entropy is proposed. The prior data of frequency hopping systems are used as the training sample space, and the Lagrange multipliers are selected as optimized variables. The Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm is used for the optimization of the dual programming of the conditional maximum entropy. Compared with the Single Threshold Method (STM) and the Double Threshold Method (DTM), the simulation results show that the proposed Maximum Entropy Method (MEM) not only has the greatest uncertainty of hopping cycle and hopping interval, it also has the lowest probability of intercept and higher environmental differentiation with threat factors. So the MEM has good RF stealth performance and it can effectively improve the survival ability of the platform.
In order to increase the interception performance of frequency hopping systems, an optimal algorithm for hopping cycle and hopping interval, which is based on the conditional maximum entropy is proposed. The prior data of frequency hopping systems are used as the training sample space, and the Lagrange multipliers are selected as optimized variables. The Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm is used for the optimization of the dual programming of the conditional maximum entropy. Compared with the Single Threshold Method (STM) and the Double Threshold Method (DTM), the simulation results show that the proposed Maximum Entropy Method (MEM) not only has the greatest uncertainty of hopping cycle and hopping interval, it also has the lowest probability of intercept and higher environmental differentiation with threat factors. So the MEM has good RF stealth performance and it can effectively improve the survival ability of the platform.
2015, 37(4): 848-854.
doi: 10.11999/JEIT140817
Abstract:
Massive MIMO technique can effectively increase system capacity in the fifth Generation (5G) cellular network, where Base Station (BS) is equipped with a very large number of antennas. Considering the impact of channel estimation error on performance, the transmission power minimization problem is formulated subject to the non-outage probability constraints of each users signal to interference plus noise ratio. In respect that the non-convex probability constraints make the downlink beamforming difficult to solve, Uplink-Downlink Duality Algorithm (UDDA) is proposed to design Coordinated BeamForming (CBF) by using the property of trace of the matrix to scale the non-convex probability constraint. To reduce the signaling overhead in Massive MIMO system, a Distributed Algorithm based on Large System Analysis (DALSA) is proposed, which only needs the large-scale channel information. The simulation results show that DALSA, in the targeted SINR constraint, not only reduces instantaneous channel state information transmission overhead in Massive MIMO system, but also performs well in robustness compared with UDDA.
Massive MIMO technique can effectively increase system capacity in the fifth Generation (5G) cellular network, where Base Station (BS) is equipped with a very large number of antennas. Considering the impact of channel estimation error on performance, the transmission power minimization problem is formulated subject to the non-outage probability constraints of each users signal to interference plus noise ratio. In respect that the non-convex probability constraints make the downlink beamforming difficult to solve, Uplink-Downlink Duality Algorithm (UDDA) is proposed to design Coordinated BeamForming (CBF) by using the property of trace of the matrix to scale the non-convex probability constraint. To reduce the signaling overhead in Massive MIMO system, a Distributed Algorithm based on Large System Analysis (DALSA) is proposed, which only needs the large-scale channel information. The simulation results show that DALSA, in the targeted SINR constraint, not only reduces instantaneous channel state information transmission overhead in Massive MIMO system, but also performs well in robustness compared with UDDA.
2015, 37(4): 855-862.
doi: 10.11999/JEIT140661
Abstract:
The Access Point (AP) in Wireless Local Area Network (WLAN) can be localized. In this paper, an AP Localization algorithm based on the Probability Density (PDAPL) is proposed. First, the region is portioned into several cells. Then, the probability of each cell is calculated according to the receive signal strength from the directional antenna in different locations and different angles, and a probability statistical table is constructed. Finally, the location of AP is assessed. Experiment results demonstrate that the proposed algorithm does not require a lot of data and can obtain relative high accuracy with a small number of measurement points and angles. Compared with DrivebyLoc, Distance, and AoA, half of or fewer measurement points and angles are needed in the case of the same accuracy with PDAPL. And for the same measurement points and angles, the accuracy of PDAPL is improved about 50% compared with DrivebyLoc.
The Access Point (AP) in Wireless Local Area Network (WLAN) can be localized. In this paper, an AP Localization algorithm based on the Probability Density (PDAPL) is proposed. First, the region is portioned into several cells. Then, the probability of each cell is calculated according to the receive signal strength from the directional antenna in different locations and different angles, and a probability statistical table is constructed. Finally, the location of AP is assessed. Experiment results demonstrate that the proposed algorithm does not require a lot of data and can obtain relative high accuracy with a small number of measurement points and angles. Compared with DrivebyLoc, Distance, and AoA, half of or fewer measurement points and angles are needed in the case of the same accuracy with PDAPL. And for the same measurement points and angles, the accuracy of PDAPL is improved about 50% compared with DrivebyLoc.
2015, 37(4): 863-867.
doi: 10.11999/JEIT140644
Abstract:
In modern wireless communication systems, multiple-antenna-transmitting in association with Orthogonal Space-Time Block Code (OSTBC) is a key technology to improve communication rate, reliability, and decoding complexity. In this paper, a modulation identification algorithm is proposed which is well suitable for the Multiple Input Single Output (MISO)-OSTBC system. First, the MISO system is transformed into a Multiple Input Multiple Output (MIMO) system by reshaping the received data. Then, maximum likelihood based approach is used to identify the modulation. Simulations validate the effectiveness of the proposed algorithm.
In modern wireless communication systems, multiple-antenna-transmitting in association with Orthogonal Space-Time Block Code (OSTBC) is a key technology to improve communication rate, reliability, and decoding complexity. In this paper, a modulation identification algorithm is proposed which is well suitable for the Multiple Input Single Output (MISO)-OSTBC system. First, the MISO system is transformed into a Multiple Input Multiple Output (MIMO) system by reshaping the received data. Then, maximum likelihood based approach is used to identify the modulation. Simulations validate the effectiveness of the proposed algorithm.
2015, 37(4): 868-873.
doi: 10.11999/JEIT140959
Abstract:
In order to enhance the spectrum utilization, this paper uses the nonlinear dynamics theory for modeling and prediction of spectrum state duration. Firstly, the real spectrum state duration is investigated. Then, this study uses the directional derivative to accomplish the state-space reconstruction of the spectrum time series with the non-uniform time delays. Finally, the Scale-Dependent Lyapunov Exponent (SDLE) is used to determine the characteristics of chaos. Based on the Davidon-Fletcher-Powell-based Second Order of Volterra Filter (DFPSOVF) method, a novel Volterra model with adaptive coefficient adjusting using Limited storage Broyden-Fletcher- Goldfarb-Shanno quasi-Newton (L-BFGS) method is proposed. Furthermore, the proposed model is applied to predict the short-term spectrum with chaotic characteristics. To reduce the complexity of this new model, the useless filter coefficients are eliminated adaptively. The numerical simulations show that the new algorithm can reduce the complexity and guarantee prediction accuracy.
In order to enhance the spectrum utilization, this paper uses the nonlinear dynamics theory for modeling and prediction of spectrum state duration. Firstly, the real spectrum state duration is investigated. Then, this study uses the directional derivative to accomplish the state-space reconstruction of the spectrum time series with the non-uniform time delays. Finally, the Scale-Dependent Lyapunov Exponent (SDLE) is used to determine the characteristics of chaos. Based on the Davidon-Fletcher-Powell-based Second Order of Volterra Filter (DFPSOVF) method, a novel Volterra model with adaptive coefficient adjusting using Limited storage Broyden-Fletcher- Goldfarb-Shanno quasi-Newton (L-BFGS) method is proposed. Furthermore, the proposed model is applied to predict the short-term spectrum with chaotic characteristics. To reduce the complexity of this new model, the useless filter coefficients are eliminated adaptively. The numerical simulations show that the new algorithm can reduce the complexity and guarantee prediction accuracy.
2015, 37(4): 874-880.
doi: 10.11999/JEIT140845
Abstract:
A prediction Vertical HandOff (M-VHO) algorithm is proposed by using a Markov process in vehicle heterogeneous network, to improve handoff performance when network status dynamic transformation after the handoff decision. It takes into account influence of network status dynamic transformation on vehicle terminals Quality of Service (QoS) after the handoff decision. Its basic idea is in that a future wireless networks status transformation is predicted by transition probability of Markov process if vertical handoff is required; otherwise, the weight of evaluating attribute parameters be determined by fuzzy logic method; finally, the total incomes of each wireless network are compare, including handoff decision incomes, handoff execution incomes and communication service incomes after handoff execution, and the optimal network is selected. The simulation results show that when the high load balanced situation is ensured the algorithm effectively improves the average blocking rate and packet loss rate of vehicle terminal, reduces the ping-pong effect, and insure the QoS of vehicle terminal.
A prediction Vertical HandOff (M-VHO) algorithm is proposed by using a Markov process in vehicle heterogeneous network, to improve handoff performance when network status dynamic transformation after the handoff decision. It takes into account influence of network status dynamic transformation on vehicle terminals Quality of Service (QoS) after the handoff decision. Its basic idea is in that a future wireless networks status transformation is predicted by transition probability of Markov process if vertical handoff is required; otherwise, the weight of evaluating attribute parameters be determined by fuzzy logic method; finally, the total incomes of each wireless network are compare, including handoff decision incomes, handoff execution incomes and communication service incomes after handoff execution, and the optimal network is selected. The simulation results show that when the high load balanced situation is ensured the algorithm effectively improves the average blocking rate and packet loss rate of vehicle terminal, reduces the ping-pong effect, and insure the QoS of vehicle terminal.
2015, 37(4): 881-886.
doi: 10.11999/JEIT140831
Abstract:
The current signature-based Grbner basis algorithms are mostly in Buchberger style and the researches related to matrix style often aim to analyze the complexity of algorithms. From a practical aspect, this paper provides a concrete Gao-Volny-Wang (GVW) algorithm in matrix style and presents optimization at the algorithmic level. Meanwhile, an efficient reduction criterion is given in the paper. Many popular criteria and strategies are compared by some experiments which show that the matrix version described in the paper is a combination of reasonable criteria and strategies. Moreover, the matrix-GVW is two to six times faster than the Buchberger style for some polynomial systems, e.g. Cyclic series and Katsura series.
The current signature-based Grbner basis algorithms are mostly in Buchberger style and the researches related to matrix style often aim to analyze the complexity of algorithms. From a practical aspect, this paper provides a concrete Gao-Volny-Wang (GVW) algorithm in matrix style and presents optimization at the algorithmic level. Meanwhile, an efficient reduction criterion is given in the paper. Many popular criteria and strategies are compared by some experiments which show that the matrix version described in the paper is a combination of reasonable criteria and strategies. Moreover, the matrix-GVW is two to six times faster than the Buchberger style for some polynomial systems, e.g. Cyclic series and Katsura series.
2015, 37(4): 887-893.
doi: 10.11999/JEIT140820
Abstract:
A novel mechanism combining Counting Bloom Filter (CBF) and Timeout Bloom Filter (TBF) is proposed, aiming at identifying IP long flow precisely. By adjusting the timeout dynamically and deleting end flows timely, the mechanism can solve the space congestion of Bloom filter and identify heavy hitters without normal end flag. The timeout and accuracy are analyzed. When adjusting the timeout dynamically according to the traffic arrival intensity and Bloom filter vector length, the mechanism can get minimum error. The experiments are conducted based on the real network trace. The results demonstrate that the proposed method is more accurate than the existing algorithms.
A novel mechanism combining Counting Bloom Filter (CBF) and Timeout Bloom Filter (TBF) is proposed, aiming at identifying IP long flow precisely. By adjusting the timeout dynamically and deleting end flows timely, the mechanism can solve the space congestion of Bloom filter and identify heavy hitters without normal end flag. The timeout and accuracy are analyzed. When adjusting the timeout dynamically according to the traffic arrival intensity and Bloom filter vector length, the mechanism can get minimum error. The experiments are conducted based on the real network trace. The results demonstrate that the proposed method is more accurate than the existing algorithms.
2015, 37(4): 894-899.
doi: 10.11999/JEIT140786
Abstract:
Bayesian network is an important theoretical tool in the artificial algorithm field, and learning structure from data is considered as NP-hard. In this article, a hybrid learning method is proposed by starting from analysis of information provided by low-order conditional independence testing. The methods of constructing boundaries of the structure space of the target network are given, as well as the complete theoretical proof. A search scoring algorithm is operated to find the final structure of the network. Simulation results show that the hybrid learning method proposed in this article has higher learning precision and is more efficient than similar algorithms.
Bayesian network is an important theoretical tool in the artificial algorithm field, and learning structure from data is considered as NP-hard. In this article, a hybrid learning method is proposed by starting from analysis of information provided by low-order conditional independence testing. The methods of constructing boundaries of the structure space of the target network are given, as well as the complete theoretical proof. A search scoring algorithm is operated to find the final structure of the network. Simulation results show that the hybrid learning method proposed in this article has higher learning precision and is more efficient than similar algorithms.
2015, 37(4): 900-906.
doi: 10.11999/JEIT140740
Abstract:
Satellite navigation systems is susceptible to jamming, Space-Time Adaptive Processing (STAP) technique has obvious advantage in the anti-jamming of navigation receiver, i.e. system Degree Of Freedom (DOF) visibly increasing, but traditional STAP would bring serious signal distortion in navigation receiver anti-jamming. In this paper, a novel STAP method based on orthogonal constraint in navigation receiver is proposed. This method modifies the constraint of traditional STAP based on the principle of STAP, avoids the influence of the delay time signal, the jamming is efficiently suppressed by STAP in the acquisition and decoding of navigation signal. Theory analysis and simulation indicate that the novel technique can solve the problem of navigation signal distortion in STAP efficiently.
Satellite navigation systems is susceptible to jamming, Space-Time Adaptive Processing (STAP) technique has obvious advantage in the anti-jamming of navigation receiver, i.e. system Degree Of Freedom (DOF) visibly increasing, but traditional STAP would bring serious signal distortion in navigation receiver anti-jamming. In this paper, a novel STAP method based on orthogonal constraint in navigation receiver is proposed. This method modifies the constraint of traditional STAP based on the principle of STAP, avoids the influence of the delay time signal, the jamming is efficiently suppressed by STAP in the acquisition and decoding of navigation signal. Theory analysis and simulation indicate that the novel technique can solve the problem of navigation signal distortion in STAP efficiently.
2015, 37(4): 907-912.
doi: 10.11999/JEIT140878
Abstract:
For the frequency estimation problem of narrow-band multi-component signal, a frequency estimation algorithm based on the sparse decomposition is proposed, which simultaneously estimates the frequency of multiple narrow-band signal. Firstly, the pre-estimation is used to get the pre-estimating frequency by using the traditional method. Then the redundant dictionary is established by using the pre-estimating frequency to obtain a sparse representation of the signal. Finally, the precise frequency estimation is achieved by the matching pursuit algorithm. The algorithm can greatly reduce the length of dictionary and the computational complexity of sparse decomposition. The proposed algorithm can provide more accurate estimation results when updating residual vector by using the global information in an iterative process, and the performance is robust in lower SNR. The simulation results verify the effectiveness and correctness of the proposed algorithm.
For the frequency estimation problem of narrow-band multi-component signal, a frequency estimation algorithm based on the sparse decomposition is proposed, which simultaneously estimates the frequency of multiple narrow-band signal. Firstly, the pre-estimation is used to get the pre-estimating frequency by using the traditional method. Then the redundant dictionary is established by using the pre-estimating frequency to obtain a sparse representation of the signal. Finally, the precise frequency estimation is achieved by the matching pursuit algorithm. The algorithm can greatly reduce the length of dictionary and the computational complexity of sparse decomposition. The proposed algorithm can provide more accurate estimation results when updating residual vector by using the global information in an iterative process, and the performance is robust in lower SNR. The simulation results verify the effectiveness and correctness of the proposed algorithm.
2015, 37(4): 913-918.
doi: 10.11999/JEIT140769
Abstract:
Jammer motion brings trouble to the dual-channel cancellation, but the good result is restricted to a limited range. The rotation motion of a jammer is periodic, the effect of rotating jammer on InSAR dual-channel cancellation is studied in this paper. Since the rotating arm is too long to put into practice, an alternative method using multi-antenna jammer is presented, the antennas emit interference signal on a time division basis. Simulation result shows that the proposed method is effective.
Jammer motion brings trouble to the dual-channel cancellation, but the good result is restricted to a limited range. The rotation motion of a jammer is periodic, the effect of rotating jammer on InSAR dual-channel cancellation is studied in this paper. Since the rotating arm is too long to put into practice, an alternative method using multi-antenna jammer is presented, the antennas emit interference signal on a time division basis. Simulation result shows that the proposed method is effective.
2015, 37(4): 919-925.
doi: 10.11999/JEIT140710
Abstract:
Nowadays 3-D reconstruction for natural scene is an important aspect of the Earth observation with SAR Tomography (TomoSAR). The general method for pattern sidelobe suppression, during the tomographic processing, is usually implemented with the weighting of uniform linear array, however at the expense of mainlobe broadening. In this paper, a minimax optimization model for cells configuration is constructed based on non-uniform linear array, to achieve the optimal peak sidelobe ratio with a fixed mainlobe width for any beam direction in the range of perspectives. For this, an objective function rasterisation is proposed and then the optimal solution can be figured out by the Sequence Quadratic Programming (SQP) with differential evolution for its unique ability of global memory. Finally, experimental results with PolSARpro polarimetric TomoSAR data validate the effectiveness of the proposal for natural scene 3-D image reconstruction.
Nowadays 3-D reconstruction for natural scene is an important aspect of the Earth observation with SAR Tomography (TomoSAR). The general method for pattern sidelobe suppression, during the tomographic processing, is usually implemented with the weighting of uniform linear array, however at the expense of mainlobe broadening. In this paper, a minimax optimization model for cells configuration is constructed based on non-uniform linear array, to achieve the optimal peak sidelobe ratio with a fixed mainlobe width for any beam direction in the range of perspectives. For this, an objective function rasterisation is proposed and then the optimal solution can be figured out by the Sequence Quadratic Programming (SQP) with differential evolution for its unique ability of global memory. Finally, experimental results with PolSARpro polarimetric TomoSAR data validate the effectiveness of the proposal for natural scene 3-D image reconstruction.
2015, 37(4): 926-932.
doi: 10.11999/JEIT140589
Abstract:
Based on the target large angle domain High Resolution Range Profile (HRRP) information of the ship formation obtained by the terminal guidance radar during its search phase, this study establishes an ergodic Spatial Hidden Markov Model (SHMM) which describes statistical relationship between the vectors in a single HRRP sample and a left to right Temporal HMM (THMM) which describes statistical relationship between HRRP samples. In comparison with the method that it only establishes a THMM model with the training data of all-round angle of one target, the proposed method makes full use of the target HRRP information of large angle domain and can improve the recognition performance. Through the simulation of the five types of ship target and the field measured data analysis of three kinds of civilian vessels show that the effectiveness of the proposed method.
Based on the target large angle domain High Resolution Range Profile (HRRP) information of the ship formation obtained by the terminal guidance radar during its search phase, this study establishes an ergodic Spatial Hidden Markov Model (SHMM) which describes statistical relationship between the vectors in a single HRRP sample and a left to right Temporal HMM (THMM) which describes statistical relationship between HRRP samples. In comparison with the method that it only establishes a THMM model with the training data of all-round angle of one target, the proposed method makes full use of the target HRRP information of large angle domain and can improve the recognition performance. Through the simulation of the five types of ship target and the field measured data analysis of three kinds of civilian vessels show that the effectiveness of the proposed method.
2015, 37(4): 933-938.
doi: 10.11999/JEIT140826
Abstract:
A novel algorithm for estimation of Direction Of Departure (DOD), Direction Of Arrival (DOA), and Doppler frequency based on bistatic MIMO radar with Co-Prime Array (CPA) is presented. The transmit and receive arrays are both composed of a pair of sparse uniform subarrays. Similarly, a pair of snapshot sequences with co-prime intervals constitutes the sampling of temporal. Three manifold matrices which contain multi-targets DODs, DOAs and Doppler frequencies respectively are estimated through tensor decomposition. From which a group of Vandermonde matrices of virtual manifold are constructed. To improve the estimation accuracy, an error depressing algorithm based on eigenvalue decomposition is proposed. Finally, the above three parameters are estimated by an Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) algorithm. The proposed algorithm offers better performance through virtual array and virtual snapshot without parameter ambiguous. It requires neither peak searching nor pairing processes, and the simulation results are presented to verify the effectiveness of the proposed algorithm.
A novel algorithm for estimation of Direction Of Departure (DOD), Direction Of Arrival (DOA), and Doppler frequency based on bistatic MIMO radar with Co-Prime Array (CPA) is presented. The transmit and receive arrays are both composed of a pair of sparse uniform subarrays. Similarly, a pair of snapshot sequences with co-prime intervals constitutes the sampling of temporal. Three manifold matrices which contain multi-targets DODs, DOAs and Doppler frequencies respectively are estimated through tensor decomposition. From which a group of Vandermonde matrices of virtual manifold are constructed. To improve the estimation accuracy, an error depressing algorithm based on eigenvalue decomposition is proposed. Finally, the above three parameters are estimated by an Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) algorithm. The proposed algorithm offers better performance through virtual array and virtual snapshot without parameter ambiguous. It requires neither peak searching nor pairing processes, and the simulation results are presented to verify the effectiveness of the proposed algorithm.
2015, 37(4): 939-945.
doi: 10.11999/JEIT140685
Abstract:
When applying to a high-precision and wide-beam airborne SAR, the frequency domain imaging algorithms are incapable of accurate Motion Compensation (MoCo), while the precise time domain approaches have the drawback of heavy computation burden. A novel imaging algorithm is proposed to figure out the problem, which combined the frequency domain methods and the time domain ones through a perturbation operation to the two-dimensional spectrum of the signal. The algorithm improves the MoCo precision with the computation efficiency being ameliorated as well, thus balancing the accuracy and efficiency when implements on the high-precision and wide-beam airborne SAR. The results of the simulation and real data are used to validate the effectiveness of the proposed algorithm in application to the high-precision and wide-beam airborne SAR.
When applying to a high-precision and wide-beam airborne SAR, the frequency domain imaging algorithms are incapable of accurate Motion Compensation (MoCo), while the precise time domain approaches have the drawback of heavy computation burden. A novel imaging algorithm is proposed to figure out the problem, which combined the frequency domain methods and the time domain ones through a perturbation operation to the two-dimensional spectrum of the signal. The algorithm improves the MoCo precision with the computation efficiency being ameliorated as well, thus balancing the accuracy and efficiency when implements on the high-precision and wide-beam airborne SAR. The results of the simulation and real data are used to validate the effectiveness of the proposed algorithm in application to the high-precision and wide-beam airborne SAR.
2015, 37(4): 946-952.
doi: 10.11999/JEIT140717
Abstract:
Take the advantage of all-directional observation of circular SAR, an approach of the outlines extraction of building footprints from the circular SAR data is presented, while the approach can also be used for estimating the ground altitude around the building. The proposed approach is as follows: firstly, the rough altitude of the ground around the building is estimated by radargrammetric Digital Elevation Model (DEM) extraction, secondly, image planes are set in certain range where the estimated altitude is in the middle, and circular SAR image of the building is obtained on each image plane. Finally, one image is selected where a closed polygon frame is formed by the double bounce scattering bright lines from the building, and the outlines of building footprints are extracted from the image, while the altitude corresponding to the imaging plane is defined as the altitude of the ground around the target building. The proposed method is validated by the circular SAR data processing by the experimental SAR airborne system at P bands.
Take the advantage of all-directional observation of circular SAR, an approach of the outlines extraction of building footprints from the circular SAR data is presented, while the approach can also be used for estimating the ground altitude around the building. The proposed approach is as follows: firstly, the rough altitude of the ground around the building is estimated by radargrammetric Digital Elevation Model (DEM) extraction, secondly, image planes are set in certain range where the estimated altitude is in the middle, and circular SAR image of the building is obtained on each image plane. Finally, one image is selected where a closed polygon frame is formed by the double bounce scattering bright lines from the building, and the outlines of building footprints are extracted from the image, while the altitude corresponding to the imaging plane is defined as the altitude of the ground around the target building. The proposed method is validated by the circular SAR data processing by the experimental SAR airborne system at P bands.
2015, 37(4): 953-960.
doi: 10.11999/JEIT140618
Abstract:
The missile-borne Synthetic Aperture Radar (SAR) usually adopts the highly squint mode and subaperture to satisfy maneuvering and the real-time processing, but the signal of highly squint SAR is coupled greatly between the azimuth and range. This issue can be solved by removing the range walk in the time domain, but costs the limitation of focus depth. Due to the depth of focus and the characteristics of subaperture, a new subaperture imaging algorithmFrequency Phase Filtering Algorithm (FPFA) is proposed in this paper. Without any approximation of the slant range, the range walk can be done in the time domain and the range curvature removed in the frequency domain. Then, a new high-order equation of phase filtering factor is introduced into the frequency domain in order to correct the azimuth-dependence. Finally, the signal is focused in the Doppler domain by SPECtral ANalysis (SPECAN) technique. Both the simulation results and real data processing validate the effectiveness of the proposed method.
The missile-borne Synthetic Aperture Radar (SAR) usually adopts the highly squint mode and subaperture to satisfy maneuvering and the real-time processing, but the signal of highly squint SAR is coupled greatly between the azimuth and range. This issue can be solved by removing the range walk in the time domain, but costs the limitation of focus depth. Due to the depth of focus and the characteristics of subaperture, a new subaperture imaging algorithmFrequency Phase Filtering Algorithm (FPFA) is proposed in this paper. Without any approximation of the slant range, the range walk can be done in the time domain and the range curvature removed in the frequency domain. Then, a new high-order equation of phase filtering factor is introduced into the frequency domain in order to correct the azimuth-dependence. Finally, the signal is focused in the Doppler domain by SPECtral ANalysis (SPECAN) technique. Both the simulation results and real data processing validate the effectiveness of the proposed method.
2015, 37(4): 961-968.
doi: 10.11999/JEIT140814
Abstract:
When radar transmits the narrowband signal to the cone-shaped target, the modulation induced by precession causes the periodic change of scattering centers Instantaneous Frequency (IF) contained in echo, which can reflect the targets geometry and structure characteristics. Aiming at this, a parameter estimation method for space cone-shaped target is proposed based on narrowband micro-Doppler modulation. First, the scattering properties of the cone-shaped target are analyzed, and the scattering centers IF variation formulas caused by precession are derived. Then, the Time-Varying AutoRegressive (TVAR) model is utilized to estimate the IF variations from the narrowband echoes of the cone-shaped target, and reassociation is implemented to fix the estimation errors. Finally, based on the properties of the IF variations of the top and bottom scattering centers and the trajectory, the target geometry and micro-motion parameters are estimated. Experiments based on the electromagnetic computation data verify the validness and accuracy of the proposed method.
When radar transmits the narrowband signal to the cone-shaped target, the modulation induced by precession causes the periodic change of scattering centers Instantaneous Frequency (IF) contained in echo, which can reflect the targets geometry and structure characteristics. Aiming at this, a parameter estimation method for space cone-shaped target is proposed based on narrowband micro-Doppler modulation. First, the scattering properties of the cone-shaped target are analyzed, and the scattering centers IF variation formulas caused by precession are derived. Then, the Time-Varying AutoRegressive (TVAR) model is utilized to estimate the IF variations from the narrowband echoes of the cone-shaped target, and reassociation is implemented to fix the estimation errors. Finally, based on the properties of the IF variations of the top and bottom scattering centers and the trajectory, the target geometry and micro-motion parameters are estimated. Experiments based on the electromagnetic computation data verify the validness and accuracy of the proposed method.
2015, 37(4): 969-974.
doi: 10.11999/JEIT140675
Abstract:
On the basis of the MultiChannel Autofocus (MCA) algorithm and the Fourier-domain MultiChannel Autofocus (FMCA) algorithm, an autofocus algorithm for airborne SAR based on Doppler-domain multichannel is proposed. The proposed autofocus algorithm is also directly derived under a linear algebraic framework, allowing the phase error to be estimated and removed in a noniterative fashion to achieve the well-focused SAR image. However, unlike MCA or FMCA applied to the image domain, the proposed autofocus algorithm is used to estimate the phase error in the range compressed azimuth Doppler domain (azimuth uncompressed). In addition, it does not require the assumption of a low-return region contained to the SAR image, which makes it applicable to the strip-map mode SAR. The processing results of strip-map SAR data in different cases demonstrate the validity and feasibility of the proposed autofocus algorithm.
On the basis of the MultiChannel Autofocus (MCA) algorithm and the Fourier-domain MultiChannel Autofocus (FMCA) algorithm, an autofocus algorithm for airborne SAR based on Doppler-domain multichannel is proposed. The proposed autofocus algorithm is also directly derived under a linear algebraic framework, allowing the phase error to be estimated and removed in a noniterative fashion to achieve the well-focused SAR image. However, unlike MCA or FMCA applied to the image domain, the proposed autofocus algorithm is used to estimate the phase error in the range compressed azimuth Doppler domain (azimuth uncompressed). In addition, it does not require the assumption of a low-return region contained to the SAR image, which makes it applicable to the strip-map mode SAR. The processing results of strip-map SAR data in different cases demonstrate the validity and feasibility of the proposed autofocus algorithm.
2015, 37(4): 975-981.
doi: 10.11999/JEIT140720
Abstract:
This paper presents a method of adaptive operating frequency selection for sky-wave Over-The-Horizon Radar (OTHR) with 2-D array by predicting Signal-to-Noise Ratio (SNR) of paths to the area of interest. With the elevation resolution of 2-D array, this method overcomes the incapability of separating multipath/multimode signals for OTHR with 1-D array. For frequency selecting, firstly, sounding equipments and 2-D array record the backscattering data, environment data and ionosphere state data. Propagation paths to the area of interest are deduced for available frequencies based on the ionosphere model and state. 2-D adaptive digital beam forming is employed to suppress interferences and predict the power of echoes. Finally, the paths SNR is calculated and the frequency of the maximum SNR is selected as the optimal operating frequency.
This paper presents a method of adaptive operating frequency selection for sky-wave Over-The-Horizon Radar (OTHR) with 2-D array by predicting Signal-to-Noise Ratio (SNR) of paths to the area of interest. With the elevation resolution of 2-D array, this method overcomes the incapability of separating multipath/multimode signals for OTHR with 1-D array. For frequency selecting, firstly, sounding equipments and 2-D array record the backscattering data, environment data and ionosphere state data. Propagation paths to the area of interest are deduced for available frequencies based on the ionosphere model and state. 2-D adaptive digital beam forming is employed to suppress interferences and predict the power of echoes. Finally, the paths SNR is calculated and the frequency of the maximum SNR is selected as the optimal operating frequency.
2015, 37(4): 982-988.
doi: 10.11999/JEIT140730
Abstract:
To improve the detection performance of marine radar, the application of multifractional Brownian motion to modeling the radar sea clutter in the scanning mode is studied. It is verified that real sea clutter data submit to a non-Gaussian distribution with asymmetry, high peak and heavy tail, and have fractal characteristics. In some cases the assumption of multifractional Brownian motion for the real data is satisfied. On the basis of this analysis, the real data are modeled by using the multifractional Brownian motion, and the time-dependent Hlder function of the real data is calculated. The results show that the Hlder exponents of different regions are not the same, and the Hlder exponent of target is significantly larger than that of the sea clutter. The research results are helpful to design a reliable detection method.
To improve the detection performance of marine radar, the application of multifractional Brownian motion to modeling the radar sea clutter in the scanning mode is studied. It is verified that real sea clutter data submit to a non-Gaussian distribution with asymmetry, high peak and heavy tail, and have fractal characteristics. In some cases the assumption of multifractional Brownian motion for the real data is satisfied. On the basis of this analysis, the real data are modeled by using the multifractional Brownian motion, and the time-dependent Hlder function of the real data is calculated. The results show that the Hlder exponents of different regions are not the same, and the Hlder exponent of target is significantly larger than that of the sea clutter. The research results are helpful to design a reliable detection method.
2015, 37(4): 989-994.
doi: 10.11999/JEIT140856
Abstract:
Considering the problem that Pulse Doppler (PD) radar is not able to track target precisely in the presence of velocity gate pull-off/in jamming and clutter environment, an algorithm for target tracking is proposed which is based on the Double Models (DM) and the Amplitude Information (AI). The algorithm establishes two tracking models: one model is based on the position and amplitude measurements, the other is based on the position, velocity, and amplitude measurements. The two models both use Probabilistic Data Association based on AI (AI-PDA) to reduce the influence of clutter as much as possible, then carry out filtering and estimating using the conventional method. If there is no velocity gate pull off/in jamming, the estimations of two models are related on position and velocity; if there is jamming, the estimations are not related. Accordingly, the chi-square test is carried out and the final estimation result is determined after analysis. The simulation results prove the effectiveness of the algorithm.
Considering the problem that Pulse Doppler (PD) radar is not able to track target precisely in the presence of velocity gate pull-off/in jamming and clutter environment, an algorithm for target tracking is proposed which is based on the Double Models (DM) and the Amplitude Information (AI). The algorithm establishes two tracking models: one model is based on the position and amplitude measurements, the other is based on the position, velocity, and amplitude measurements. The two models both use Probabilistic Data Association based on AI (AI-PDA) to reduce the influence of clutter as much as possible, then carry out filtering and estimating using the conventional method. If there is no velocity gate pull off/in jamming, the estimations of two models are related on position and velocity; if there is jamming, the estimations are not related. Accordingly, the chi-square test is carried out and the final estimation result is determined after analysis. The simulation results prove the effectiveness of the algorithm.
2015, 37(4): 995-1001.
doi: 10.11999/JEIT140600
Abstract:
In order to extract effectively the discriminant characteristics of hyperspectral remote sensing image data, this paper presents a Semi-Supervised Laplace Discriminant Embedding (SSLDE) algorithm based on the discriminant information of labeled samples and the local structural information of unlabeled samples. The proposed algorithm makes use of the class information of labeled samples to maintain the separability of sample set, and discovers the local manifold structure in sample set by constructing Laplace matrix of labeled and unlabeled samples, which can achieve semi-supervised manifold discriminant. The experimental results on KSC and Urban database show that the algorithm has higher classification accuracy and can effectively extract the information of discriminant characteristics. In the overall classification accuracy, this algorithm is improved by 6.3%~7.4% compared with Semi-Supervised Maximum Margin Criterion (SSMMC) algorithm and increased by 1.6%~4.4% compared with Semi-Supervised Sub-Manifold Preserving Embedding (SSSMPE) algorithm.
In order to extract effectively the discriminant characteristics of hyperspectral remote sensing image data, this paper presents a Semi-Supervised Laplace Discriminant Embedding (SSLDE) algorithm based on the discriminant information of labeled samples and the local structural information of unlabeled samples. The proposed algorithm makes use of the class information of labeled samples to maintain the separability of sample set, and discovers the local manifold structure in sample set by constructing Laplace matrix of labeled and unlabeled samples, which can achieve semi-supervised manifold discriminant. The experimental results on KSC and Urban database show that the algorithm has higher classification accuracy and can effectively extract the information of discriminant characteristics. In the overall classification accuracy, this algorithm is improved by 6.3%~7.4% compared with Semi-Supervised Maximum Margin Criterion (SSMMC) algorithm and increased by 1.6%~4.4% compared with Semi-Supervised Sub-Manifold Preserving Embedding (SSSMPE) algorithm.
2015, 37(4): 1002-1007.
doi: 10.11999/JEIT140807
Abstract:
The near-field scattering characteristics of dielectric targets are studied in the terahertz band based on the generalized Kirchhoff impedance boundary conditions and the physical optics method. The formula of the near field scattering for dielectric targets is deduced. In the light of the increase in calculation amount caused by the shorter wavelength, a fast computational method using surface element as the calculating unit and pixel as occlusion judgment unit is proposed for the near field scattering computation in terahertz band. The method ensures the calculation accuracy and reduces greatly the computational complexity and the time consumption of occlusion judging. The calculation of the near-field Radar Cross Section (RCS) produced by a dielectric cylinder and a duck mouth scatterer is performed in terahertz band. Meanwhile, the effect of the phase on the near field RCS in different distances and frequency is analyzed.
The near-field scattering characteristics of dielectric targets are studied in the terahertz band based on the generalized Kirchhoff impedance boundary conditions and the physical optics method. The formula of the near field scattering for dielectric targets is deduced. In the light of the increase in calculation amount caused by the shorter wavelength, a fast computational method using surface element as the calculating unit and pixel as occlusion judgment unit is proposed for the near field scattering computation in terahertz band. The method ensures the calculation accuracy and reduces greatly the computational complexity and the time consumption of occlusion judging. The calculation of the near-field Radar Cross Section (RCS) produced by a dielectric cylinder and a duck mouth scatterer is performed in terahertz band. Meanwhile, the effect of the phase on the near field RCS in different distances and frequency is analyzed.
2015, 37(4): 1008-1012.
doi: 10.11999/JEIT140816
Abstract:
An ultra-wideband fractal antenna is proposed, which has an isosceles trapezoid ground-plane. Increasing fractal iterations and optimizing ground-plane shape can achieve a better impedance matching so as to realize Ultra-WideBand (UWB) antenna performance. The presented fractal antenna offers a -10 dB return loss bandwidth in the range of 4.2 to 17.5 GHz (relative band is 122.6%) with electrical dimension of 0.350.35. Thus, this type of antenna is suitable for C, X, Ku and UWB band communications, which may have a wide application prospect. The measured results fit well with the simulation results, and the effectiveness is validated using the measured results.
An ultra-wideband fractal antenna is proposed, which has an isosceles trapezoid ground-plane. Increasing fractal iterations and optimizing ground-plane shape can achieve a better impedance matching so as to realize Ultra-WideBand (UWB) antenna performance. The presented fractal antenna offers a -10 dB return loss bandwidth in the range of 4.2 to 17.5 GHz (relative band is 122.6%) with electrical dimension of 0.350.35. Thus, this type of antenna is suitable for C, X, Ku and UWB band communications, which may have a wide application prospect. The measured results fit well with the simulation results, and the effectiveness is validated using the measured results.
An Empirical Model for Wind Speed Inversion Directly from High Frequency Surface Wave Radar Sea Echo
2015, 37(4): 1013-1016.
doi: 10.11999/JEIT140850
Abstract:
The information of the significant wave height is needed in the wind speed inversion from the sea echo of High Frequency Surface Wave Radar (HFSWR) by using the empirical model of wind waves. Therefore, the accuracy of the significant wave height has an effect on the wind speed inversion. Based on the empirical model of wind waves, a wind speed inversion empirical model for wind retrieval without the information of wave height is developed, which uses the relationship between the wind speed and ratio of second-order to first-order spectrum energies. The inversion model is applied to the wind speed extraction. And the data obtained from two radars with different frequency in different detecting area are analyzed. The results show that the proposed model can be used to extract the wind speed from the HFSWR sea echo and the results of which three-parameter model are better than the two-parameter model.
The information of the significant wave height is needed in the wind speed inversion from the sea echo of High Frequency Surface Wave Radar (HFSWR) by using the empirical model of wind waves. Therefore, the accuracy of the significant wave height has an effect on the wind speed inversion. Based on the empirical model of wind waves, a wind speed inversion empirical model for wind retrieval without the information of wave height is developed, which uses the relationship between the wind speed and ratio of second-order to first-order spectrum energies. The inversion model is applied to the wind speed extraction. And the data obtained from two radars with different frequency in different detecting area are analyzed. The results show that the proposed model can be used to extract the wind speed from the HFSWR sea echo and the results of which three-parameter model are better than the two-parameter model.
