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2015 Vol. 37, No. 1
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2015, 37(1): 1-8.
doi: 10.11999/JEIT140315
Abstract:
Traditional frequency agility ISAR imaging method suffers from high sidelobe and low resolution. To improve the resolution, by exploiting the sparsity of targets in the received echo, this paper uses the sparse Bayesian algorithm, namely Expansion-Compression Variance-component based method (ExCoV), to reconstruct the ISAR image from the original Compressed Sensing (CS) ISAR data. By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse recover algorithm under the Bayesian framework can reconstruct the scatter coefficient better than the traditional methods. Different from the Sparse Bayesian Learning (SBL) endowing variance-components to all elements, the ExCoV only endows variance-components to the significant signal elements. This leads to much less parameters and faster implementation of the ExCoV than the SBL. The simulation results indicate that it can conquer the problem brought by traditional methods and achieve high precision agility ISAR imaging under the low SNR.
Traditional frequency agility ISAR imaging method suffers from high sidelobe and low resolution. To improve the resolution, by exploiting the sparsity of targets in the received echo, this paper uses the sparse Bayesian algorithm, namely Expansion-Compression Variance-component based method (ExCoV), to reconstruct the ISAR image from the original Compressed Sensing (CS) ISAR data. By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse recover algorithm under the Bayesian framework can reconstruct the scatter coefficient better than the traditional methods. Different from the Sparse Bayesian Learning (SBL) endowing variance-components to all elements, the ExCoV only endows variance-components to the significant signal elements. This leads to much less parameters and faster implementation of the ExCoV than the SBL. The simulation results indicate that it can conquer the problem brought by traditional methods and achieve high precision agility ISAR imaging under the low SNR.
2015, 37(1): 9-14.
doi: 10.11999/JEIT140202
Abstract:
For distributed aperture coherent radar with cooperative architecture and pulse trains, the signal mode in fully coherent work mode is proposed firstly. Then the analysis formula of output Signal-to-Noise Ratio gain (oSNRg) is derived, and the performance bound of oSNRg is developed by using polynomial fitting based on Cramer-Rao Bounds (CRB) of coherence parameters estimation. It is concluded that more transmitters or pulses make higher oSNRg. However, as the number of receivers increase, if the input SNR is high sufficiently, the oSNRg will gradually improve; if not, it will worsen. Finally, numerical examples demonstrate the validity of the theoretical results.
For distributed aperture coherent radar with cooperative architecture and pulse trains, the signal mode in fully coherent work mode is proposed firstly. Then the analysis formula of output Signal-to-Noise Ratio gain (oSNRg) is derived, and the performance bound of oSNRg is developed by using polynomial fitting based on Cramer-Rao Bounds (CRB) of coherence parameters estimation. It is concluded that more transmitters or pulses make higher oSNRg. However, as the number of receivers increase, if the input SNR is high sufficiently, the oSNRg will gradually improve; if not, it will worsen. Finally, numerical examples demonstrate the validity of the theoretical results.
2015, 37(1): 15-21.
doi: 10.11999/JEIT140290
Abstract:
Concerned with the influence of multipath, this paper proposes a spatially differencing smoothing technique for the low-elevation estimation in the bistatic MIMO radar under the spatially colored noise. Firstly, the multipath environment for a low-elevation target in the bistatic MIMO radar is modeled, by considering the specular reflection of the transmitter and receiver. The diffuse reflection is assumed to be the spatially colored noise. Then, the covariance matrix differencing is used to eliminate the unknown noise component, and the transmitting array and receiving array are spatially smoothed to decorrelate the multipath signals, which does the spatially differencing smoothing operation. Finally, the Direction of Departures (DODs) and Direction of Arrivals (DOAs) are estimated by unitary Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) algorithm. This paper also points to the rank deficiency problem of the spatially differencing smoothed covariance matrix in a special case, and modifies the spatially differencing smoothing method correspondingly. The proposed methods require a small number of antenna elements, fit for general unknown noise fields and low SNR environment, and solve the angle-merging problem in joint DOD and DOA estimation. The simulation results demonstrate the effectiveness of the proposed method.
Concerned with the influence of multipath, this paper proposes a spatially differencing smoothing technique for the low-elevation estimation in the bistatic MIMO radar under the spatially colored noise. Firstly, the multipath environment for a low-elevation target in the bistatic MIMO radar is modeled, by considering the specular reflection of the transmitter and receiver. The diffuse reflection is assumed to be the spatially colored noise. Then, the covariance matrix differencing is used to eliminate the unknown noise component, and the transmitting array and receiving array are spatially smoothed to decorrelate the multipath signals, which does the spatially differencing smoothing operation. Finally, the Direction of Departures (DODs) and Direction of Arrivals (DOAs) are estimated by unitary Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) algorithm. This paper also points to the rank deficiency problem of the spatially differencing smoothed covariance matrix in a special case, and modifies the spatially differencing smoothing method correspondingly. The proposed methods require a small number of antenna elements, fit for general unknown noise fields and low SNR environment, and solve the angle-merging problem in joint DOD and DOA estimation. The simulation results demonstrate the effectiveness of the proposed method.
2015, 37(1): 22-28.
doi: 10.11999/JEIT140550
Abstract:
The model-based target decomposition is one of the most fundamental applications to the polarimetry SAR. However, it can only retrieve the power information of three scattering mechanisms with polarimetric SAR data. By applying this model-based decomposition technique to the mode Compact Polarimetric SAR Interferometry (C-PolInSAR) data and decomposing the cross correlation matrix, this study can obtain the power contributions of three scattering mechanisms as well as their scattering phase center. Firstly, this study introduces the models corresponding to the three scattering mechanisms under mode C-PolInSAR observation. Then, the targets can be decomposed by the numerical method, and finally both power contributions and scattering center of mechanisms are retrieved. The simulation data validate the effectiveness of the decomposition algorithm and the impacts of different wave-band and different ground parameters on the decomposition results are analyzed.
The model-based target decomposition is one of the most fundamental applications to the polarimetry SAR. However, it can only retrieve the power information of three scattering mechanisms with polarimetric SAR data. By applying this model-based decomposition technique to the mode Compact Polarimetric SAR Interferometry (C-PolInSAR) data and decomposing the cross correlation matrix, this study can obtain the power contributions of three scattering mechanisms as well as their scattering phase center. Firstly, this study introduces the models corresponding to the three scattering mechanisms under mode C-PolInSAR observation. Then, the targets can be decomposed by the numerical method, and finally both power contributions and scattering center of mechanisms are retrieved. The simulation data validate the effectiveness of the decomposition algorithm and the impacts of different wave-band and different ground parameters on the decomposition results are analyzed.
2015, 37(1): 29-36.
doi: 10.11999/JEIT140129
Abstract:
In target recognition community, when dealing with large-scale and complex distributed data, it is very expensive to train a classifier using all input data and the underlying structure of the data is ignored. To overcome these limitations, the Mixture-of-Experts (ME) system is proposed, which partitions the input data into several clusters and learns a classifier for each cluster. However, in the traditional ME system, the number of experts are fixed in advance and clustering procedure and the classification tasks are de-coupled. To deal with these problems, a Dirichlet Process mixture of Latent Variable Support Vector Machine (DPLVSVM) is proposed. In DPLVSVM model, the number of clusters is chosen automatically by DP mixture model, and the linear Latent Variable SVMs (LVSVM) are employed in each cluster. Different from previous algorithms, in DPLVSVM, the clustering procedure and LVSVM are jointly learned to gain infinite discriminative clusters. And the parameters can be inferred simply and effectively via Gibbs sampling technique. Based on the experimental data obtained from the synthesized dataset, Benchmark datasets and measured radar echo data, the effectiveness of proposed method is validated.
In target recognition community, when dealing with large-scale and complex distributed data, it is very expensive to train a classifier using all input data and the underlying structure of the data is ignored. To overcome these limitations, the Mixture-of-Experts (ME) system is proposed, which partitions the input data into several clusters and learns a classifier for each cluster. However, in the traditional ME system, the number of experts are fixed in advance and clustering procedure and the classification tasks are de-coupled. To deal with these problems, a Dirichlet Process mixture of Latent Variable Support Vector Machine (DPLVSVM) is proposed. In DPLVSVM model, the number of clusters is chosen automatically by DP mixture model, and the linear Latent Variable SVMs (LVSVM) are employed in each cluster. Different from previous algorithms, in DPLVSVM, the clustering procedure and LVSVM are jointly learned to gain infinite discriminative clusters. And the parameters can be inferred simply and effectively via Gibbs sampling technique. Based on the experimental data obtained from the synthesized dataset, Benchmark datasets and measured radar echo data, the effectiveness of proposed method is validated.
2015, 37(1): 37-42.
doi: 10.11999/JEIT140314
Abstract:
Due to the disadvantages of a traditional GPS receiver in the environment of weak signal are that slow convergence rate and serious tracking error, a GPS signal tracking algorithm is proposed, in which the coherent integration of I/Q branch is considered as measurement of Unscented Kalman Filter (UKF). The filter model of the baseband signal processing is constructed and tracking loop is closed by UKF. Then the signal parameters of GPS tracking loop can be accurately estimated. Moreover, the anti-jamming capability and tracking ability of receiver are improved in the presence of weak signals. Simulation results demonstrate that the tracking ability and convergence rate of proposed algorithm outperform traditional method in the case of different Carrier to Noise ratio (C/N).
Due to the disadvantages of a traditional GPS receiver in the environment of weak signal are that slow convergence rate and serious tracking error, a GPS signal tracking algorithm is proposed, in which the coherent integration of I/Q branch is considered as measurement of Unscented Kalman Filter (UKF). The filter model of the baseband signal processing is constructed and tracking loop is closed by UKF. Then the signal parameters of GPS tracking loop can be accurately estimated. Moreover, the anti-jamming capability and tracking ability of receiver are improved in the presence of weak signals. Simulation results demonstrate that the tracking ability and convergence rate of proposed algorithm outperform traditional method in the case of different Carrier to Noise ratio (C/N).
2015, 37(1): 43-49.
doi: 10.11999/JEIT140238
Abstract:
In order to meet the needs of high speed and real-time, all-weather, high-precision and large-scale ranging, the method of continuous microwave radar with amplitude modulation is proposed. Based on the analysis of the characteristics of the main microwave radar ranging methods, the mathematical relationships among the modulation frequency, measurement range and its accuracy are deeply discussed in the method, in which the low single frequency signal modulated on high frequency carrier signal is adopted to get large-scale ranging and also the time-measurement technology with high precision is used to achieve high precision and high speed ranging. Finally, the experimental radar system is set up by the mixer and the time-measurement chip TDC-GP2 etc. The experiments of the phase difference measurement unit based on TDC-GP2 show its accuracy is2.7110-4degrees. When the frequency of carrier signal is 2.4 GHz and that of modulation signal is 150 kHz, the experimental results under the distance from 3.0～4.1 m verify the effectiveness that the radar system could achieve large-scale ranging of 1000 m, and show that the ranging accuracy is 0.0187 m of distance of 3.0 m. Moreover, the average ranging time for single measuring point is about 0.02～0.03 s.
In order to meet the needs of high speed and real-time, all-weather, high-precision and large-scale ranging, the method of continuous microwave radar with amplitude modulation is proposed. Based on the analysis of the characteristics of the main microwave radar ranging methods, the mathematical relationships among the modulation frequency, measurement range and its accuracy are deeply discussed in the method, in which the low single frequency signal modulated on high frequency carrier signal is adopted to get large-scale ranging and also the time-measurement technology with high precision is used to achieve high precision and high speed ranging. Finally, the experimental radar system is set up by the mixer and the time-measurement chip TDC-GP2 etc. The experiments of the phase difference measurement unit based on TDC-GP2 show its accuracy is2.7110-4degrees. When the frequency of carrier signal is 2.4 GHz and that of modulation signal is 150 kHz, the experimental results under the distance from 3.0～4.1 m verify the effectiveness that the radar system could achieve large-scale ranging of 1000 m, and show that the ranging accuracy is 0.0187 m of distance of 3.0 m. Moreover, the average ranging time for single measuring point is about 0.02～0.03 s.
2015, 37(1): 50-55.
doi: 10.11999/JEIT140244
Abstract:
The topology information of the targets observed by sensors can be used to solve the track association problem under the condition of systematic bias. However, the traditional algorithms dont make full use of track information and are not fit for the presence of sensors false alarm and missing detect. An anti-bias track association algorithm based on topology statistical distance is proposed. First, the target state estimation and covariance is converted to acquire the topology description in the coordinates of the reference target. Then the global optimization association is realized based on the derivation of topology statistical distance. Finally, the average statistic distance of neighboring target association pairs in the coordinates of the reference target is applied as the association degree of the reference targets, and the reference targets association judgment is accomplished according to the double threshold rule. The simulation results show that the performance of the proposed algorithm is apparently better than the traditional algorithm under the conditions of dense formation, random distributed targets and the presence of sensors false alarm and missing detection.
The topology information of the targets observed by sensors can be used to solve the track association problem under the condition of systematic bias. However, the traditional algorithms dont make full use of track information and are not fit for the presence of sensors false alarm and missing detect. An anti-bias track association algorithm based on topology statistical distance is proposed. First, the target state estimation and covariance is converted to acquire the topology description in the coordinates of the reference target. Then the global optimization association is realized based on the derivation of topology statistical distance. Finally, the average statistic distance of neighboring target association pairs in the coordinates of the reference target is applied as the association degree of the reference targets, and the reference targets association judgment is accomplished according to the double threshold rule. The simulation results show that the performance of the proposed algorithm is apparently better than the traditional algorithm under the conditions of dense formation, random distributed targets and the presence of sensors false alarm and missing detection.
2015, 37(1): 56-62.
doi: 10.11999/JEIT140230
Abstract:
For multi-target tracking in heavily cluttered environment, the number of measurement-to-track association hypotheses in each scan grows rapidly in traditional Centralized Multi-Sensor Multiple Hypothesis Tracking (CMS-MHT) method, which leads that the uncertainty of data association greatly increases such that correct association can hardly be given using traditional track score resulting in high leakage rate and effects of track splitting. Based on the space distribution characteristics of false alarm and target measurement when the sensor measurement error is small, for target tracking using multiple sensors of same type this paper proposes a new angle aided CMS-MHT method, which designs angle aided track score computation to reduce the uncertainty of measurement-to-track association. In such a way, the proposed angle aided CMS-MHT can provide better association hypotheses compared with traditional CMS-MHT. The experimental results illustrate that angle aided CMS-MHT reduces leakage rate and has better track integrity in heavily cluttered environment.
For multi-target tracking in heavily cluttered environment, the number of measurement-to-track association hypotheses in each scan grows rapidly in traditional Centralized Multi-Sensor Multiple Hypothesis Tracking (CMS-MHT) method, which leads that the uncertainty of data association greatly increases such that correct association can hardly be given using traditional track score resulting in high leakage rate and effects of track splitting. Based on the space distribution characteristics of false alarm and target measurement when the sensor measurement error is small, for target tracking using multiple sensors of same type this paper proposes a new angle aided CMS-MHT method, which designs angle aided track score computation to reduce the uncertainty of measurement-to-track association. In such a way, the proposed angle aided CMS-MHT can provide better association hypotheses compared with traditional CMS-MHT. The experimental results illustrate that angle aided CMS-MHT reduces leakage rate and has better track integrity in heavily cluttered environment.
2015, 37(1): 63-70.
doi: 10.11999/JEIT140143
Abstract:
In order to reserve ship targets and reduce sea clutters as the false alarms from the SAR Regions Of Interest (ROI) chips, a ship discrimination feature named Target Pixel Aggregative Measure (TPAM) is proposed in this paper. Benefited from the technology of change detection, TPAM using the gray difference in SAR imagery to separate the target pixels and background pixels. Firstly, based on the assumption that the central pixels of a ROI belong to target pixels while the surrounding pixels fall into sea clutters, a change detection measure based on the likelihood ratio is used to generate the residual data. Then the target pixels and background pixels are automatically separated and produce a binary image by the KSW entropy method. Finally, the center of the binary image is used as a seed to implement region growing and TPAM can be obtained to discriminate targets and clutters. Experimental results using RADARSAT-1 SAR data show that the propose discrimination feature is not only simple and robust, but also has a strong differentiate ability, which can eliminate most of false alarms effectively.
In order to reserve ship targets and reduce sea clutters as the false alarms from the SAR Regions Of Interest (ROI) chips, a ship discrimination feature named Target Pixel Aggregative Measure (TPAM) is proposed in this paper. Benefited from the technology of change detection, TPAM using the gray difference in SAR imagery to separate the target pixels and background pixels. Firstly, based on the assumption that the central pixels of a ROI belong to target pixels while the surrounding pixels fall into sea clutters, a change detection measure based on the likelihood ratio is used to generate the residual data. Then the target pixels and background pixels are automatically separated and produce a binary image by the KSW entropy method. Finally, the center of the binary image is used as a seed to implement region growing and TPAM can be obtained to discriminate targets and clutters. Experimental results using RADARSAT-1 SAR data show that the propose discrimination feature is not only simple and robust, but also has a strong differentiate ability, which can eliminate most of false alarms effectively.
2015, 37(1): 71-77.
doi: 10.11999/JEIT140121
Abstract:
From the SAR imaging model, combining the scattering center theory, this paper estimates scattering centers of high resolution image from the low resolution image under the conditions of sparse. The Region Of Interesting (ROI) can be reconstructed by several sinc functions and the super resolution section is obtained after side lobe suppression. Based on the Nonlinear Least Squares (NLS) estimation, an iterative algorithm is employed to solve the super resolution reconstruction problem and the simulations are based on TerraSAR-X measurement data. Simulation results show that the proposed method is able to get higher spatial resolution and Target to Clutter Ratio (TCR) values as compared with bicubic interpolation and 1 norm regularization method. The analysis results show that the accuracy of the algorithm is affected by both the Signal to Noise Ratio (SNR) and the rebuilding 3 dB bandwidth of sinc function, the higher 3 dB bandwidth tends to be more robust to noise.
From the SAR imaging model, combining the scattering center theory, this paper estimates scattering centers of high resolution image from the low resolution image under the conditions of sparse. The Region Of Interesting (ROI) can be reconstructed by several sinc functions and the super resolution section is obtained after side lobe suppression. Based on the Nonlinear Least Squares (NLS) estimation, an iterative algorithm is employed to solve the super resolution reconstruction problem and the simulations are based on TerraSAR-X measurement data. Simulation results show that the proposed method is able to get higher spatial resolution and Target to Clutter Ratio (TCR) values as compared with bicubic interpolation and 1 norm regularization method. The analysis results show that the accuracy of the algorithm is affected by both the Signal to Noise Ratio (SNR) and the rebuilding 3 dB bandwidth of sinc function, the higher 3 dB bandwidth tends to be more robust to noise.
2015, 37(1): 78-84.
doi: 10.11999/JEIT140214
Abstract:
How to reduce the storage and transmission cost of mass hyperspectral data is concerned with growing interest. This paper proposes a hyperspectral data compression algorithm using sparse representation. First, a training sample set is constructed with a band selection algorithm, and then all hyperspectral bands are coded sparsely using a basis function dictionary learned from the training set. Finally, the position indices and values of the non-zero elements are entropy coded to finish the compression. Experimental results reveal that the proposal algorithm achieves better nonlinear approximation performance than 3D-DWT and outperforms 3D-SPIHT. Besides, the algorithm has better performance in spectral information preservation.
How to reduce the storage and transmission cost of mass hyperspectral data is concerned with growing interest. This paper proposes a hyperspectral data compression algorithm using sparse representation. First, a training sample set is constructed with a band selection algorithm, and then all hyperspectral bands are coded sparsely using a basis function dictionary learned from the training set. Finally, the position indices and values of the non-zero elements are entropy coded to finish the compression. Experimental results reveal that the proposal algorithm achieves better nonlinear approximation performance than 3D-DWT and outperforms 3D-SPIHT. Besides, the algorithm has better performance in spectral information preservation.
2015, 37(1): 85-90.
doi: 10.11999/JEIT140313
Abstract:
A novel Time Differences Of Arrival (TDOA) localization algorithm based on linear-correction is proposed to address the nonlinear problem of TDOA positioning. Firstly, the proposed algorithm reformulates the highly nonlinear TDOA equations into a set of pseudo-linear ones and the Weighted Least Squares (WLS) estimator is used to obtain the initial emitter position estimation. Then a linear weighted least squares problem with respect to the estimation bias is formulated through utilizing first-order Taylor-series expansion to the pseudo-linear equations. The effectiveness of the proposed method is theoretically analyzed under small noise postulation. Finally, the extension to the general case of moving emitter with constant acceleration localization scenario is also presented and the corresponding estimation accuracy can achieve the Cramer-Rao Lower Bound (CRLB) as well. Computer simulation results demonstrate the effectiveness of the proposed algorithm.
A novel Time Differences Of Arrival (TDOA) localization algorithm based on linear-correction is proposed to address the nonlinear problem of TDOA positioning. Firstly, the proposed algorithm reformulates the highly nonlinear TDOA equations into a set of pseudo-linear ones and the Weighted Least Squares (WLS) estimator is used to obtain the initial emitter position estimation. Then a linear weighted least squares problem with respect to the estimation bias is formulated through utilizing first-order Taylor-series expansion to the pseudo-linear equations. The effectiveness of the proposed method is theoretically analyzed under small noise postulation. Finally, the extension to the general case of moving emitter with constant acceleration localization scenario is also presented and the corresponding estimation accuracy can achieve the Cramer-Rao Lower Bound (CRLB) as well. Computer simulation results demonstrate the effectiveness of the proposed algorithm.
2015, 37(1): 91-96.
doi: 10.11999/JEIT140281
Abstract:
NYquist Folding Receiver (NYFR) is a novel ultra-wideband receiver structure, which can sample a large range wideband signal by using a single ADC. The Synchronous NYquist Folding Receiver (SNYFR) is an improved structure of NYFR. In this paper, a new SNYFR structure with dual local oscillator is proposed and its mathematical model with the input of multi-component LFM signal is deduced. meanwhile, under the input of multi-component LFM signal, a Nyquist Zone (NZ) judgment and parameters estimation algorithm based on combination of Sinusoidal Frequency Modulation Matching (SFMM) and fractional domain folding compensation is proposed to overcome the difficulties of single local oscillator SNYFR structure in dealing with multi-component LFM signal and to improve noise immunity. Computer simulations show that effective detection of multi- component LFM signal and accurate parameters estimation can be achieved by using the proposed algorithm.
NYquist Folding Receiver (NYFR) is a novel ultra-wideband receiver structure, which can sample a large range wideband signal by using a single ADC. The Synchronous NYquist Folding Receiver (SNYFR) is an improved structure of NYFR. In this paper, a new SNYFR structure with dual local oscillator is proposed and its mathematical model with the input of multi-component LFM signal is deduced. meanwhile, under the input of multi-component LFM signal, a Nyquist Zone (NZ) judgment and parameters estimation algorithm based on combination of Sinusoidal Frequency Modulation Matching (SFMM) and fractional domain folding compensation is proposed to overcome the difficulties of single local oscillator SNYFR structure in dealing with multi-component LFM signal and to improve noise immunity. Computer simulations show that effective detection of multi- component LFM signal and accurate parameters estimation can be achieved by using the proposed algorithm.
2015, 37(1): 97-102.
doi: 10.11999/JEIT140169
Abstract:
From a Bayesian perspective, the commonly used sparse recovery algorithms, including Sparse Bayesian Learning (SBL), Regularized FOCUSS (R_FOCUSS) and Log-Sum, are compared. The analysis shows that, as a special case of latent variable Bayesian models, SBL, which operates in latent variable space via type-II maximum likelihood method, can be viewed as a more general and flexible means, and offers an avenue for improvement when finding sparse solutions to underdetermined inverse problems. Numerical results demonstrate the superior performance of SBL as compared to state-of-the-art sparse methods based on type-I maximum likelihood.
From a Bayesian perspective, the commonly used sparse recovery algorithms, including Sparse Bayesian Learning (SBL), Regularized FOCUSS (R_FOCUSS) and Log-Sum, are compared. The analysis shows that, as a special case of latent variable Bayesian models, SBL, which operates in latent variable space via type-II maximum likelihood method, can be viewed as a more general and flexible means, and offers an avenue for improvement when finding sparse solutions to underdetermined inverse problems. Numerical results demonstrate the superior performance of SBL as compared to state-of-the-art sparse methods based on type-I maximum likelihood.
2015, 37(1): 103-109.
doi: 10.11999/JEIT140140
Abstract:
Weak signal detection and high SNR seismic image generation are primary tasks in detailed sub-bottom profile detection. After analyzing the principle of deconvolution based on Fractional Fourier Transform (FrFT) and deriving the formula of time dimensional transformation, a new detailed sub-bottom profile detection algorithm based on FrFT is proposed. The fractional Fourier domain (u domain) sub-bottom impulse response is achieved by u domain deconvolution and the intraband SNR is increased by u domain windowed filtering technique, then high SNR envelop of u domain sediment impulse response envelop is transformed to time domain by time dimensional transformation to get high quality sub-bottom profile. Simulation and experimental data processing validate the validity of the algorithm in intraband denoising and detailed detection, and its performance is better than pulse compression and AutoRegressive (AR) forecast filtering.
Weak signal detection and high SNR seismic image generation are primary tasks in detailed sub-bottom profile detection. After analyzing the principle of deconvolution based on Fractional Fourier Transform (FrFT) and deriving the formula of time dimensional transformation, a new detailed sub-bottom profile detection algorithm based on FrFT is proposed. The fractional Fourier domain (u domain) sub-bottom impulse response is achieved by u domain deconvolution and the intraband SNR is increased by u domain windowed filtering technique, then high SNR envelop of u domain sediment impulse response envelop is transformed to time domain by time dimensional transformation to get high quality sub-bottom profile. Simulation and experimental data processing validate the validity of the algorithm in intraband denoising and detailed detection, and its performance is better than pulse compression and AutoRegressive (AR) forecast filtering.
2015, 37(1): 110-115.
doi: 10.11999/JEIT140329
Abstract:
For modeling and solving problems of complex nonlinear systems whose input/output have uncertainty and are associated with time or process, a cloud process neural network model is built in the paper. It has uncertainty information processing ability by combining process neural networks processing ability for time-varying signal with cloud model transformation ability between qualitative and quantitative concepts. In addition, the cat swarm optimization algorithm is used to optimize the network structure and parameters simultaneously, and it helps to improve network approximation?and generalization performance. The effective extension of neural networks in time domain and uncertain information processing field is realized. Experimental results verify the effectiveness and feasibility of the model and algorithm.
For modeling and solving problems of complex nonlinear systems whose input/output have uncertainty and are associated with time or process, a cloud process neural network model is built in the paper. It has uncertainty information processing ability by combining process neural networks processing ability for time-varying signal with cloud model transformation ability between qualitative and quantitative concepts. In addition, the cat swarm optimization algorithm is used to optimize the network structure and parameters simultaneously, and it helps to improve network approximation?and generalization performance. The effective extension of neural networks in time domain and uncertain information processing field is realized. Experimental results verify the effectiveness and feasibility of the model and algorithm.
2015, 37(1): 116-122.
doi: 10.11999/JEIT140218
Abstract:
Considering the limitation of the well-known multiple model formulation of the Random Finite Set (RFS) that the statistics characteristic of clutter is assumed to be known a priori, this paper proposes a new multiple maneuvering target tracking algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density Filter (GMCPHDF) in the case of unknown clutter. The proposed method predicts the intensity function of actual target states by Best-Fitting Gaussian (BFG) approximation, which is independent of the target motion model. Then the closed-loop iteration procedure among the intensity function of actual target states, the mean number of clutter generators, and the hybrid cardinality distribution of actual targets and clutter generators is established. The simulation results show that the proposed method can effectively estimate the target number, target states and the mean number of clutters simultaneously.
Considering the limitation of the well-known multiple model formulation of the Random Finite Set (RFS) that the statistics characteristic of clutter is assumed to be known a priori, this paper proposes a new multiple maneuvering target tracking algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density Filter (GMCPHDF) in the case of unknown clutter. The proposed method predicts the intensity function of actual target states by Best-Fitting Gaussian (BFG) approximation, which is independent of the target motion model. Then the closed-loop iteration procedure among the intensity function of actual target states, the mean number of clutter generators, and the hybrid cardinality distribution of actual targets and clutter generators is established. The simulation results show that the proposed method can effectively estimate the target number, target states and the mean number of clutters simultaneously.
2015, 37(1): 123-129.
doi: 10.11999/JEIT140053
Abstract:
Many measures, e.g., Maximal Information Coefficient (MIC), are presented to identify interesting correlations for pairs of variables, but few for triplets or even for higher dimension variable set. Based on that, the Maximal Information Entropy (MIE) is proposed for measuring the general correlation of a multivariable data set. For k variables, firstly, the maximal information matrix is constructed according to the MIC scores of any pairs of variables; then, maximal information entropy, which measures the correlation degree of the concerned k variables, is calculated based on the maximal information matrix. The simulation experimental results show that MIE can detect one-dimensional manifold dependence of triplets. The applications to real datasets further verify the feasibility of MIE.
Many measures, e.g., Maximal Information Coefficient (MIC), are presented to identify interesting correlations for pairs of variables, but few for triplets or even for higher dimension variable set. Based on that, the Maximal Information Entropy (MIE) is proposed for measuring the general correlation of a multivariable data set. For k variables, firstly, the maximal information matrix is constructed according to the MIC scores of any pairs of variables; then, maximal information entropy, which measures the correlation degree of the concerned k variables, is calculated based on the maximal information matrix. The simulation experimental results show that MIE can detect one-dimensional manifold dependence of triplets. The applications to real datasets further verify the feasibility of MIE.
2015, 37(1): 130-136.
doi: 10.11999/JEIT140119
Abstract:
In the field of saliency detection, background prior has become a novel viewpoint, but how to identify the real background is challenging. In this paper, a background-identified method is proposed based on homology continuity using the extracted background features, and the identified background is applied to the following computation, improving the eventual saliency map in accuracy as well as correctness. First, the primary saliency of each superpixel produced by Mean Shift (MS) segmentation algorithm is calculated. Second, 4 edges are extracted to generate their RGB histograms, and the Euclidean distance between each two of the histograms is calculated, if the distance is smaller than a given value, these two edges are defined to be continual and more likely to be the real background. Finally, the pixels saliency is calculated using the prior background knowledge to figure the final saliency map. The results show that the proposed method outperforms other algorithms in accuracy and efficiency.
In the field of saliency detection, background prior has become a novel viewpoint, but how to identify the real background is challenging. In this paper, a background-identified method is proposed based on homology continuity using the extracted background features, and the identified background is applied to the following computation, improving the eventual saliency map in accuracy as well as correctness. First, the primary saliency of each superpixel produced by Mean Shift (MS) segmentation algorithm is calculated. Second, 4 edges are extracted to generate their RGB histograms, and the Euclidean distance between each two of the histograms is calculated, if the distance is smaller than a given value, these two edges are defined to be continual and more likely to be the real background. Finally, the pixels saliency is calculated using the prior background knowledge to figure the final saliency map. The results show that the proposed method outperforms other algorithms in accuracy and efficiency.
2015, 37(1): 137-142.
doi: 10.11999/JEIT140197
Abstract:
Most of the existing Independent Component Analysis (ICA) based motion detection algorithms use a single observation vector generation method and two-channel data for motion detection, which make the traditional algorithms unable to obtain a more complete and accurate state of the moving objects. In this paper, four different observation vector generation methods are proposed and larger channel numbers are introduced into traditional ICA. The motion characteristics of the moving objects are covered more widely and more information for foreground extraction is obtained from the multi-channel data. These improvements make ICA be able to deal with indistinguishable and slowly moving objects. The quantitative evaluation from different experiments shows that the multi-channel data and the combination of four observation vector generation methods enable ICA to achieve a better performance with a reasonable cost of tiny increase on false alarms.
Most of the existing Independent Component Analysis (ICA) based motion detection algorithms use a single observation vector generation method and two-channel data for motion detection, which make the traditional algorithms unable to obtain a more complete and accurate state of the moving objects. In this paper, four different observation vector generation methods are proposed and larger channel numbers are introduced into traditional ICA. The motion characteristics of the moving objects are covered more widely and more information for foreground extraction is obtained from the multi-channel data. These improvements make ICA be able to deal with indistinguishable and slowly moving objects. The quantitative evaluation from different experiments shows that the multi-channel data and the combination of four observation vector generation methods enable ICA to achieve a better performance with a reasonable cost of tiny increase on false alarms.
2015, 37(1): 143-149.
doi: 10.11999/JEIT140262
Abstract:
The current mainstream intelligent Fuzzing often constructs new test samples through precise analysis of the programs internal structure, which is heavily dependent on the performance of the computer and often overlooks the guiding significance of the discovered program information of exceptions for construction of new testing samples. To overcome these shortcomings, this paper presents a method based on intelligent Fuzzing exception distribution steering, which establishes a data-constructing model named TGM (Testcase Generation Model) for binary program testing. Firstly the relevant information of testing samples is collected according to the computing capability. Then random initial testing samples are selected for testing. Finally, the testing results are used to initialize parameters of the model, which guides the priority selection of more effective input attributes to construct new samples for the next round of testing. This procedure is repeated in iterative testing to constantly update model parameters for guiding the next testing. Experimental data shows that this method can assist Fuzzing to prioritize more effective samples for testing. Design prototyping tool CombFuzz has good performance in the exception detection capability and code coverage capability, meanwhile, when the tests are carried out on large programs, compared with MiniFuzz of Microsoft,s SDL lab, this method increases the average of exception detection rate by nearly 18 times in a limited period of time, and has found 7 undisclosed exploitable vulnerabilities in WPS 2013 and other softwares that MiniFuzz did not find.
The current mainstream intelligent Fuzzing often constructs new test samples through precise analysis of the programs internal structure, which is heavily dependent on the performance of the computer and often overlooks the guiding significance of the discovered program information of exceptions for construction of new testing samples. To overcome these shortcomings, this paper presents a method based on intelligent Fuzzing exception distribution steering, which establishes a data-constructing model named TGM (Testcase Generation Model) for binary program testing. Firstly the relevant information of testing samples is collected according to the computing capability. Then random initial testing samples are selected for testing. Finally, the testing results are used to initialize parameters of the model, which guides the priority selection of more effective input attributes to construct new samples for the next round of testing. This procedure is repeated in iterative testing to constantly update model parameters for guiding the next testing. Experimental data shows that this method can assist Fuzzing to prioritize more effective samples for testing. Design prototyping tool CombFuzz has good performance in the exception detection capability and code coverage capability, meanwhile, when the tests are carried out on large programs, compared with MiniFuzz of Microsoft,s SDL lab, this method increases the average of exception detection rate by nearly 18 times in a limited period of time, and has found 7 undisclosed exploitable vulnerabilities in WPS 2013 and other softwares that MiniFuzz did not find.
2015, 37(1): 150-157.
doi: 10.11999/JEIT140091
Abstract:
Traditional encryption schemes are largely based on the two stepsconfusion and diffusion proposed by Shannon. It is proved by experiments that encryption designed on bit level is more efficient. An encryption scheme based on DNA coding, codon grouping and substitution is proposed in this paper, which belongs to bit level encryption. Original media is encoded into pseudo codon sequence with the ideology of DNA coding and central dogma in molecular biology. Then codons are grouped together with a random grouping method. After that, an arbitrary N-nary system is constructed with the pseudo codon sequence and the grouping strategy. Information that controls the substitution of codons is generated with a hyper-chaotic system and the arbitrary N-nary system. Finally, substitution of codons in the pseudo codon sequence is imposed to accomplish the encryption. Both theoretical analysis and experimental results show that the proposed method has much better performance.
Traditional encryption schemes are largely based on the two stepsconfusion and diffusion proposed by Shannon. It is proved by experiments that encryption designed on bit level is more efficient. An encryption scheme based on DNA coding, codon grouping and substitution is proposed in this paper, which belongs to bit level encryption. Original media is encoded into pseudo codon sequence with the ideology of DNA coding and central dogma in molecular biology. Then codons are grouped together with a random grouping method. After that, an arbitrary N-nary system is constructed with the pseudo codon sequence and the grouping strategy. Information that controls the substitution of codons is generated with a hyper-chaotic system and the arbitrary N-nary system. Finally, substitution of codons in the pseudo codon sequence is imposed to accomplish the encryption. Both theoretical analysis and experimental results show that the proposed method has much better performance.
2015, 37(1): 158-162.
doi: 10.11999/JEIT140326
Abstract:
In a typical cognitive wireless network, multiple secondary users compete to rent for the sub-bands in the authorized spectrum of primary users to transfer data. This paper focuses on simultaneously maximizing the payoff of both primary users and secondary users by jointly optimizing transmit powers of secondary users, sub-band allocation of secondary users, and pricing coefficients of primary users. Specifically, based on backwards induction, this paper decomposes the whole game into three sub optimization problems, i.e., power control problem, sub-band allocation problem, and price adjustment problem. These problems are then solved one by one to obtain the sub-game perfect Nash equilibrium of the whole game. Finally, this paper proposes an algorithm to search for Nash equilibrium. Simulation results verify the theoretical deduction results and effectiveness of the proposed algorithm.
In a typical cognitive wireless network, multiple secondary users compete to rent for the sub-bands in the authorized spectrum of primary users to transfer data. This paper focuses on simultaneously maximizing the payoff of both primary users and secondary users by jointly optimizing transmit powers of secondary users, sub-band allocation of secondary users, and pricing coefficients of primary users. Specifically, based on backwards induction, this paper decomposes the whole game into three sub optimization problems, i.e., power control problem, sub-band allocation problem, and price adjustment problem. These problems are then solved one by one to obtain the sub-game perfect Nash equilibrium of the whole game. Finally, this paper proposes an algorithm to search for Nash equilibrium. Simulation results verify the theoretical deduction results and effectiveness of the proposed algorithm.
2015, 37(1): 163-168.
doi: 10.11999/JEIT140141
Abstract:
The outage probability of a dual-hop MIMO Amplify-and-Forward (AF) relay network with beamforming technique is investigated, where the source, relay and destination are all equipped with multiple antennas. By supposing that the Maximal-Ratio-Transmission (MRT) and Maximal-Ratio-Combining (MRC) are applied at the transmitter and receiver of each hop, the output Signal-to-Interference-plus-Noise Ratio (SINR) of the dual-hop AF relay system with multiple Co-Channel Interferences (CCIs) and noise at the relay is obtained, and the new closed-form expression of Outage Probability (OP) is derived for the fixed-gain relaying system. The computer simulation results show the effectiveness of the performance analysis; meanwhile, the results are able to provide valuable insights on the effects of key parameters on the performance of the dual-hop AF relaying system, as well as the benefit of implementing multiple antennas.
The outage probability of a dual-hop MIMO Amplify-and-Forward (AF) relay network with beamforming technique is investigated, where the source, relay and destination are all equipped with multiple antennas. By supposing that the Maximal-Ratio-Transmission (MRT) and Maximal-Ratio-Combining (MRC) are applied at the transmitter and receiver of each hop, the output Signal-to-Interference-plus-Noise Ratio (SINR) of the dual-hop AF relay system with multiple Co-Channel Interferences (CCIs) and noise at the relay is obtained, and the new closed-form expression of Outage Probability (OP) is derived for the fixed-gain relaying system. The computer simulation results show the effectiveness of the performance analysis; meanwhile, the results are able to provide valuable insights on the effects of key parameters on the performance of the dual-hop AF relaying system, as well as the benefit of implementing multiple antennas.
2015, 37(1): 169-174.
doi: 10.11999/JEIT140247
Abstract:
In order to improve the reconstruction accuracy of smoothed l0-norm (Sl0) algorithm in the presence of noise, a modified algorithm named smoothed l0-norm regularized least-square (l2-Sl0) is proposed in this paper, which permits a small perturbation. Further, through placing a finite grid in the planar time-frequency bounded region, the problem of doubly-selective slow-fading channel estimation in OFDM system is modeled as the problem of sparse signal reconstruction in compressed sensing framework, and then thel2-Sl0 algorithm is applied to reconstruct the channel parameters. A number of computer-simulation-based experiments show that reconstruction accuracy of thel2-Sl0 algorithm is improved by approximately 10 dB as compared with theSl0 algorithm in the presence of noise. The performance of the proposed doubly-selective slow-fading channel estimation method usingl2-Sl0 algorithm is nearly close to that of the ideal Least Square (ideal-LS) method. Moreover, the proposed method has higher estimation uccuracy well in the case of low SNR.
In order to improve the reconstruction accuracy of smoothed l0-norm (Sl0) algorithm in the presence of noise, a modified algorithm named smoothed l0-norm regularized least-square (l2-Sl0) is proposed in this paper, which permits a small perturbation. Further, through placing a finite grid in the planar time-frequency bounded region, the problem of doubly-selective slow-fading channel estimation in OFDM system is modeled as the problem of sparse signal reconstruction in compressed sensing framework, and then thel2-Sl0 algorithm is applied to reconstruct the channel parameters. A number of computer-simulation-based experiments show that reconstruction accuracy of thel2-Sl0 algorithm is improved by approximately 10 dB as compared with theSl0 algorithm in the presence of noise. The performance of the proposed doubly-selective slow-fading channel estimation method usingl2-Sl0 algorithm is nearly close to that of the ideal Least Square (ideal-LS) method. Moreover, the proposed method has higher estimation uccuracy well in the case of low SNR.
2015, 37(1): 175-181.
doi: 10.11999/JEIT140001
Abstract:
Traditional Soft Frequency Reuse (SFR) can not adapt to dynamic traffic distribution in Long Term Evolution (LTE) systems, leading to imbalanced spectral efficiency in cell center and edge area. In order to solve this problem, this paper proposes a Dynamic Spectrum Allocation scheme for LTE system by exploiting Cognitive capability (Cog-DSA). By exploiting inter base station cooperation, spectrum use status is acquired. Then the available spectrum set is determined and co-channel interference from adjacent cells is evaluated. Finally, dynamic spectrum borrowing and service base station selection are implemented. Simulation results demonstrate that the proposed Cog-DSA method can effectively improve the spectrum utilization, mitigate inter-cell interference and significantly raise cell edge users' transmission rate.
Traditional Soft Frequency Reuse (SFR) can not adapt to dynamic traffic distribution in Long Term Evolution (LTE) systems, leading to imbalanced spectral efficiency in cell center and edge area. In order to solve this problem, this paper proposes a Dynamic Spectrum Allocation scheme for LTE system by exploiting Cognitive capability (Cog-DSA). By exploiting inter base station cooperation, spectrum use status is acquired. Then the available spectrum set is determined and co-channel interference from adjacent cells is evaluated. Finally, dynamic spectrum borrowing and service base station selection are implemented. Simulation results demonstrate that the proposed Cog-DSA method can effectively improve the spectrum utilization, mitigate inter-cell interference and significantly raise cell edge users' transmission rate.
2015, 37(1): 182-187.
doi: 10.11999/JEIT140267
Abstract:
The design of soft-input soft-output Frequency Domain Equalizer (FDE) is explored by using message passing methods for MIMO Single Carrier-Cyclic Prefix (SC-CP) systems. A novel low-complexity MIMO Turbo frequency domain equalization scheme is proposed based on the Generalized Approximate Message Passing (GAMP) algorithm. This new message passing MIMO equalization approach can circumvent the problem of MIMO matrix inversion on each frequency bin involved in the conventional MIMO FDE, while retaining the merits of frequency domain equalization due to SC-CP transmission. Thus, it has a complexity that grows linearly with the number of receive antennas. Simulation results show that the proposed MIMO equalization algorithm can significantly outperform the conventional counterpart in the terms of Bit Error Rate (BER) performance.
The design of soft-input soft-output Frequency Domain Equalizer (FDE) is explored by using message passing methods for MIMO Single Carrier-Cyclic Prefix (SC-CP) systems. A novel low-complexity MIMO Turbo frequency domain equalization scheme is proposed based on the Generalized Approximate Message Passing (GAMP) algorithm. This new message passing MIMO equalization approach can circumvent the problem of MIMO matrix inversion on each frequency bin involved in the conventional MIMO FDE, while retaining the merits of frequency domain equalization due to SC-CP transmission. Thus, it has a complexity that grows linearly with the number of receive antennas. Simulation results show that the proposed MIMO equalization algorithm can significantly outperform the conventional counterpart in the terms of Bit Error Rate (BER) performance.
2015, 37(1): 188-192.
doi: 10.11999/JEIT140089
Abstract:
Using multiple transmit antennas to guarantee the information security is one of the common methods in the field of physical layer security communication. However, this method leads to the transmit signal with the drawback of high Peak-to-Average Power Ratio (PAPR), which degrades the communication performance for the desired receiver. In this paper, the PAPR conception for security communication signal based on the weighted antenna array is defined from the point view of weighted value. The degradation performance of the desired receiver is also analyzed. In addition, a PAPR reduction algorithm is proposed for the physical layer security communication system using a random antenna array. Simulation results show that the proposed algorithm reduces the PAPR value for the security communication signal under the condition that the security performance does not reduce.
Using multiple transmit antennas to guarantee the information security is one of the common methods in the field of physical layer security communication. However, this method leads to the transmit signal with the drawback of high Peak-to-Average Power Ratio (PAPR), which degrades the communication performance for the desired receiver. In this paper, the PAPR conception for security communication signal based on the weighted antenna array is defined from the point view of weighted value. The degradation performance of the desired receiver is also analyzed. In addition, a PAPR reduction algorithm is proposed for the physical layer security communication system using a random antenna array. Simulation results show that the proposed algorithm reduces the PAPR value for the security communication signal under the condition that the security performance does not reduce.
2015, 37(1): 193-199.
doi: 10.11999/JEIT140309
Abstract:
In wireless networks, when using traditional game studying physical layer security, the energy-limited transmission nodes tend to choose a non-cooperative strategy in order to maximize their own secrecy rate, resulting in reduced network secrecy rate. To solve this problem, this paper presents a method for physical layer security cooperation based on evolutionary game. Firstly, this study defines strategies (sending artificial noise or signal) and benefits (secrecy rate under different strategy combination) according to evolutionary game; then, the transmission nodes adjust strategy to maximize benefits based on current network state and difference between cooperation utility and average expected utility; finally, the conditions that the transmitting nodes can achieve stable cooperation, are obtained, and the network evolution from an unstable state to a stable collaboration state improves the secrecy rate of the system. The simulation and analysis results show that under the Gaussian channel conditions, compared to traditional game method, the proposed methods network secrecy rate can be improved 1 bit/(s Hz).
In wireless networks, when using traditional game studying physical layer security, the energy-limited transmission nodes tend to choose a non-cooperative strategy in order to maximize their own secrecy rate, resulting in reduced network secrecy rate. To solve this problem, this paper presents a method for physical layer security cooperation based on evolutionary game. Firstly, this study defines strategies (sending artificial noise or signal) and benefits (secrecy rate under different strategy combination) according to evolutionary game; then, the transmission nodes adjust strategy to maximize benefits based on current network state and difference between cooperation utility and average expected utility; finally, the conditions that the transmitting nodes can achieve stable cooperation, are obtained, and the network evolution from an unstable state to a stable collaboration state improves the secrecy rate of the system. The simulation and analysis results show that under the Gaussian channel conditions, compared to traditional game method, the proposed methods network secrecy rate can be improved 1 bit/(s Hz).
2015, 37(1): 200-205.
doi: 10.11999/JEIT140175
Abstract:
According to the data spatial correlation of Wireless Sensor Networks (WSNs), this study proposes a clustering mechanism based on the data gradient. In the proposed clustering mechanism, the cluster head nodes maintain Auto Regressive (AR) prediction model of the sensory data within each cluster in the time domain. Moreover, the cluster head nodes adjust the temporal sampling frequency based on the implementation of above predicted adaptive algorithm model. By adjusting the temporal sampling frequency, the redundant data transmission is reduced as well as ensuring desired sampling accuracy, so as energy efficiency is improved. The temporal sampling frequency adjustment algorithm takes into account spatial and temporal combined correlation characteristics of sensory data. As a result, the simulation results demonstrate the performance benefits of the proposed algorithm.
According to the data spatial correlation of Wireless Sensor Networks (WSNs), this study proposes a clustering mechanism based on the data gradient. In the proposed clustering mechanism, the cluster head nodes maintain Auto Regressive (AR) prediction model of the sensory data within each cluster in the time domain. Moreover, the cluster head nodes adjust the temporal sampling frequency based on the implementation of above predicted adaptive algorithm model. By adjusting the temporal sampling frequency, the redundant data transmission is reduced as well as ensuring desired sampling accuracy, so as energy efficiency is improved. The temporal sampling frequency adjustment algorithm takes into account spatial and temporal combined correlation characteristics of sensory data. As a result, the simulation results demonstrate the performance benefits of the proposed algorithm.
2015, 37(1): 206-213.
doi: 10.11999/JEIT140104
Abstract:
Discrete Cosine Transform (DCT) plays an important role in the codec process of video signals, and has a significant influence on the compression efficiency and quality. In this paper, a Coarse-Grained Reconfigurable Array (CGRA) based hardware architecture is proposed for 8-point 2D DCT. Through the reconfiguration of coarse-grained reconfigurable array, the proposed architecture is capable of supporting 88 2D discrete cosine transform of the multiple video compression coding standards in a single platform. The experimental results show that the proposed architecture is able to parallel process 8 pixels in a cycle, and the throughput achieves up to 1.157109 pixels per second. The design efficiency and power efficiency is about 4.33 times and 12.3 times higher than existing works respectively. Moreover, the proposed architecture can support real-time decoding of 40962048 at 30 fps (4:2:0) video sequences.
Discrete Cosine Transform (DCT) plays an important role in the codec process of video signals, and has a significant influence on the compression efficiency and quality. In this paper, a Coarse-Grained Reconfigurable Array (CGRA) based hardware architecture is proposed for 8-point 2D DCT. Through the reconfiguration of coarse-grained reconfigurable array, the proposed architecture is capable of supporting 88 2D discrete cosine transform of the multiple video compression coding standards in a single platform. The experimental results show that the proposed architecture is able to parallel process 8 pixels in a cycle, and the throughput achieves up to 1.157109 pixels per second. The design efficiency and power efficiency is about 4.33 times and 12.3 times higher than existing works respectively. Moreover, the proposed architecture can support real-time decoding of 40962048 at 30 fps (4:2:0) video sequences.
2015, 37(1): 214-219.
doi: 10.11999/JEIT140210
Abstract:
Based on the sub-cell Finite-Difference Time-Domain (FDTD) method, an equivalent field-to-line coupling numerical model for transmission line common-mode electromagnetic leakage is provided to calculate the inductive coupling and capacitive coupling between parallel transmission lines. The new model can solve the coupling crosstalk problem under inhomogeneous media and other complex situations. In addition, the presented work describes such method for numerical calculation, which has a more concise form. With the new model, simulation of common-mode electromagnetic information leakage for parallel transmission line is implemented. Furthermore, both simulation in time domain and experimental results in frequency domain show that the numerical model can effectively describe the emission characteristics of common-mode electromagnetic information leakage for parallel transmission lines.
Based on the sub-cell Finite-Difference Time-Domain (FDTD) method, an equivalent field-to-line coupling numerical model for transmission line common-mode electromagnetic leakage is provided to calculate the inductive coupling and capacitive coupling between parallel transmission lines. The new model can solve the coupling crosstalk problem under inhomogeneous media and other complex situations. In addition, the presented work describes such method for numerical calculation, which has a more concise form. With the new model, simulation of common-mode electromagnetic information leakage for parallel transmission line is implemented. Furthermore, both simulation in time domain and experimental results in frequency domain show that the numerical model can effectively describe the emission characteristics of common-mode electromagnetic information leakage for parallel transmission lines.
2015, 37(1): 220-225.
doi: 10.11999/JEIT140192
Abstract:
The highly nonlinearity and non-uniqueness of the geoelectric model constrain the application of simulated annealing method in the Time-domain ElectroMagnetic (TEM) explorations. A simulating annealing inversion method is presented to get the geoelectric model from the data of the TEM method. Firstly, by the dual digital filtering method, the fast and accurate forward problem is settled. Furthermore, the inversion is converted into an optimization issue and the simulating annealing inversion method is applied to the search for the global optimal solution, of which the search step can be adjusted adaptively, hence improving the searching effeciency. The comparison results with conventional inversion methods show that the proposed method can achieve the global optimal solution and a more accurate result of the geoelectric model.
The highly nonlinearity and non-uniqueness of the geoelectric model constrain the application of simulated annealing method in the Time-domain ElectroMagnetic (TEM) explorations. A simulating annealing inversion method is presented to get the geoelectric model from the data of the TEM method. Firstly, by the dual digital filtering method, the fast and accurate forward problem is settled. Furthermore, the inversion is converted into an optimization issue and the simulating annealing inversion method is applied to the search for the global optimal solution, of which the search step can be adjusted adaptively, hence improving the searching effeciency. The comparison results with conventional inversion methods show that the proposed method can achieve the global optimal solution and a more accurate result of the geoelectric model.
2015, 37(1): 236-240.
doi: 10.11999/JEIT140127
Abstract:
A basic premise for Modulated Wideband Converter (MWC) applying to spectrum sensing is that the signal spectrum is sparse. Otherwise, the reconstruction result will be incorrect. A method of judging whether the reconstruction is successful is proposed for MWC compressed sensing. It utilizes the correlation between two consecutive reconstructed subband energy. Simulation results show that the method can judge whether the reconstruction is successful with high accuracy, and can reduce the interference probability with primary users when the spectrum is not that sparse.
A basic premise for Modulated Wideband Converter (MWC) applying to spectrum sensing is that the signal spectrum is sparse. Otherwise, the reconstruction result will be incorrect. A method of judging whether the reconstruction is successful is proposed for MWC compressed sensing. It utilizes the correlation between two consecutive reconstructed subband energy. Simulation results show that the method can judge whether the reconstruction is successful with high accuracy, and can reduce the interference probability with primary users when the spectrum is not that sparse.
2015, 37(1): 226-235.
doi: 10.11999/JEIT141156
Abstract:
Since the birth of the high-speed railway, it is growing with the mobile communication demand of train to wayside. One part of traffic load demand is from the train control and train dispatching, which is an important part of the high-speed railway. And another part of the traffic load is from the passengers, which is one of the important Key Performance Indexes (KFIs) that can satisfy the requirement of passenger service quality. Especially in the era of mobile Internet, the Internet demand becomes an important part of peoples daily life. This paper summarizes the important processes of the development of mobile communications for high-speed railway over the world, especially the present mobile communication technologies to support the passengers information access from train to Internet. Based on the development trend of mobile communication technology, the development trend of its counterpart in high-speed railway is discussed, including its relationship with the development of public mobile communication technology. Some future key technologies, including some detailed suggestions, of mobile communications for high-speed railway are prospected, which may provide some references for the relevant researchers in this field.
Since the birth of the high-speed railway, it is growing with the mobile communication demand of train to wayside. One part of traffic load demand is from the train control and train dispatching, which is an important part of the high-speed railway. And another part of the traffic load is from the passengers, which is one of the important Key Performance Indexes (KFIs) that can satisfy the requirement of passenger service quality. Especially in the era of mobile Internet, the Internet demand becomes an important part of peoples daily life. This paper summarizes the important processes of the development of mobile communications for high-speed railway over the world, especially the present mobile communication technologies to support the passengers information access from train to Internet. Based on the development trend of mobile communication technology, the development trend of its counterpart in high-speed railway is discussed, including its relationship with the development of public mobile communication technology. Some future key technologies, including some detailed suggestions, of mobile communications for high-speed railway are prospected, which may provide some references for the relevant researchers in this field.
