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2019 Vol. 41, No. 12
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2019, 41(12): 2795-2801.
doi: 10.11999/JEIT181165
Abstract:
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
2019, 41(12): 2802-2809.
doi: 10.11999/JEIT190001
Abstract:
Long Range(LoRa) Backscattering Communication (BC) not only has the advantages of low cost and low power consumption, but also has a long communication distance. However, the existing LoRa BC scheme is complex and can not be applied to actual engineering. For this purpose, a new LoRa BC method is proposed. A Direct Digital frequency Synthesis (DDS) technique is used to generate a square wave with a linear frequency variation as a LoRa scattering modulation signal. For the first time, the prototype of LoRa BC system based on MCU is demonstrated. Experimental results show that design can successfully realize backscatter communication at any position between the station and the receiver which are 208 meters apart, while being compatible with commodity LoRa chipset. In addition, the method is also applicable to an Application Specific Integrated Circuit (ASIC) design, which enables the LoRa backscattering IC to have higher robustness, lower cost, and lower power consumption.
Long Range(LoRa) Backscattering Communication (BC) not only has the advantages of low cost and low power consumption, but also has a long communication distance. However, the existing LoRa BC scheme is complex and can not be applied to actual engineering. For this purpose, a new LoRa BC method is proposed. A Direct Digital frequency Synthesis (DDS) technique is used to generate a square wave with a linear frequency variation as a LoRa scattering modulation signal. For the first time, the prototype of LoRa BC system based on MCU is demonstrated. Experimental results show that design can successfully realize backscatter communication at any position between the station and the receiver which are 208 meters apart, while being compatible with commodity LoRa chipset. In addition, the method is also applicable to an Application Specific Integrated Circuit (ASIC) design, which enables the LoRa backscattering IC to have higher robustness, lower cost, and lower power consumption.
2019, 41(12): 2810-2816.
doi: 10.11999/JEIT190171
Abstract:
In order to solve the problem of the Device to Device (D2D) multi-multiplex communication resource blocks allocation in a cell, the resource blocks allocation scheme about D2D multi-multiplex mode based on non-equilibrium solution is proposed after analyzing a D2D user to multiplex two and three cells respectively. The problem of resource blocks partitioning is transformed into the problem of solving the joint revenue maximum value of the multiplexed cellular user by using game theory. When the Nash equilibrium solution does not exist, the objective function is analyzed, the "optimal solution" is solved in the feasible domain and the optimality of unbalanced solution processing is guaranteed. When the equilibrium solution exists, it is rounded up and used as the basis of the resource allocation scheme to maintain its optimality. The theoretical analysis and simulation results show that the proposed algorithm enhances significantly the system performance and sum rate.
In order to solve the problem of the Device to Device (D2D) multi-multiplex communication resource blocks allocation in a cell, the resource blocks allocation scheme about D2D multi-multiplex mode based on non-equilibrium solution is proposed after analyzing a D2D user to multiplex two and three cells respectively. The problem of resource blocks partitioning is transformed into the problem of solving the joint revenue maximum value of the multiplexed cellular user by using game theory. When the Nash equilibrium solution does not exist, the objective function is analyzed, the "optimal solution" is solved in the feasible domain and the optimality of unbalanced solution processing is guaranteed. When the equilibrium solution exists, it is rounded up and used as the basis of the resource allocation scheme to maintain its optimality. The theoretical analysis and simulation results show that the proposed algorithm enhances significantly the system performance and sum rate.
2019, 41(12): 2817-2825.
doi: 10.11999/JEIT181168
Abstract:
Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the Fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, and the sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the Fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, and the sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
2019, 41(12): 2826-2835.
doi: 10.11999/JEIT190173
Abstract:
For the problem of sparse feature enhancement in Synthetic Aperture Radar (SAR) imagery, conventional methods are difficult to achieve a preferable balance between accuracy and efficiency. In this paper, a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM) is proposed for general SAR imaging feature enhancement within complex raw data domain. The problem is firstly imposed by an augmented Lagrange function, and the complex\begin{document}${\ell _1}$\end{document} ![]()
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For the problem of sparse feature enhancement in Synthetic Aperture Radar (SAR) imagery, conventional methods are difficult to achieve a preferable balance between accuracy and efficiency. In this paper, a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM) is proposed for general SAR imaging feature enhancement within complex raw data domain. The problem is firstly imposed by an augmented Lagrange function, and the complex
2019, 41(12): 2836-2843.
doi: 10.11999/JEIT190148
Abstract:
The generalized Pareto distributed sea clutter model, known as one of the compound-Gaussian models, is able to describe heavy-tailed characteristic of sea clutter under high-resolution and low grazing angle detection scene efficiently, and the accuracy of parameter estimation under this condition heavily impacts radar’s detection property. In this paper, Combined BiPercentile (CBiP) estimator is proposed to estimate the parameters. The CBiP estimator is realized based on the explicit roots of low-order polynomial equations and full application of sample information in returns, which provides a highly-accurate parameter estimation process. Besides, the CBiP estimator can maintain the robustness of estimation performance when outliers with extremely large power are existing in samples, while other estimators, including moment-based and Maximum Likelihood (ML) estimators, degrade extremely in estimation accuracy. Without outliers in samples, the combined bipercentile estimator shows similar accuracy with the ML estimator. With outliers, the combined percentile estimator is the only method with robustness in performance, compared with other estimators aforementioned. Moreover, the ability of the new estimator is verified by measured clutter data.
The generalized Pareto distributed sea clutter model, known as one of the compound-Gaussian models, is able to describe heavy-tailed characteristic of sea clutter under high-resolution and low grazing angle detection scene efficiently, and the accuracy of parameter estimation under this condition heavily impacts radar’s detection property. In this paper, Combined BiPercentile (CBiP) estimator is proposed to estimate the parameters. The CBiP estimator is realized based on the explicit roots of low-order polynomial equations and full application of sample information in returns, which provides a highly-accurate parameter estimation process. Besides, the CBiP estimator can maintain the robustness of estimation performance when outliers with extremely large power are existing in samples, while other estimators, including moment-based and Maximum Likelihood (ML) estimators, degrade extremely in estimation accuracy. Without outliers in samples, the combined bipercentile estimator shows similar accuracy with the ML estimator. With outliers, the combined percentile estimator is the only method with robustness in performance, compared with other estimators aforementioned. Moreover, the ability of the new estimator is verified by measured clutter data.
2019, 41(12): 2844-2851.
doi: 10.11999/JEIT181133
Abstract:
The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong concave function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the proposed model can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong concave function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the proposed model can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
2019, 41(12): 2852-2858.
doi: 10.11999/JEIT190096
Abstract:
Forward-looking Synthetic Aperture Radar (SAR) imaging has the problem of left-right Doppler ambiguity, so it is necessary to use spatial resources for ambiguity resolution. Due to the weight and size of Unmanned Aerial Vehicle (UAV), the receiving array is usually small, and the ability of spatial beam-forming for Doppler ambiguity resolution is insufficient. In addition, the small Doppler gradient and narrow bandwidth of forward-looking SAR echo make the receiving bandwidth underutilized. Based on the above problems, a Doppler diversity Multiple Input Multiple Output (MIMO) forward-looking SAR imaging method is proposed. Based on the forward-looking SAR imaging technology, the narrow-band forward-looking Doppler echo is modulated to different Doppler centers by using Doppler diversity MIMO technology to make full use of the Doppler receiving bandwidth. Furthermore, a virtual receiving array with several times the aperture of the real receiving array can be obtained, which expands greatly the receiving channel and improves effectively the performance of forward-looking SAR imaging in de-Doppler left-right ambiguity.
Forward-looking Synthetic Aperture Radar (SAR) imaging has the problem of left-right Doppler ambiguity, so it is necessary to use spatial resources for ambiguity resolution. Due to the weight and size of Unmanned Aerial Vehicle (UAV), the receiving array is usually small, and the ability of spatial beam-forming for Doppler ambiguity resolution is insufficient. In addition, the small Doppler gradient and narrow bandwidth of forward-looking SAR echo make the receiving bandwidth underutilized. Based on the above problems, a Doppler diversity Multiple Input Multiple Output (MIMO) forward-looking SAR imaging method is proposed. Based on the forward-looking SAR imaging technology, the narrow-band forward-looking Doppler echo is modulated to different Doppler centers by using Doppler diversity MIMO technology to make full use of the Doppler receiving bandwidth. Furthermore, a virtual receiving array with several times the aperture of the real receiving array can be obtained, which expands greatly the receiving channel and improves effectively the performance of forward-looking SAR imaging in de-Doppler left-right ambiguity.
2019, 41(12): 2859-2864.
doi: 10.11999/JEIT190136
Abstract:
The performance of a Constant False Alarm Rate (CFAR) detector is often evaluated in three typical backgrounds - homogeneous environment, multiple targets situation and clutter edges described by Prof. Rohling. However, there is a lack of the analytic expression of the false alarm rate for the Rank Sum (RS) nonparametric detector at clutter boundaries, and lack of a comparison of the ability for the RS detector to control the rise of the false alarm rate at clutter edges to that of the conventional parametric CFAR schemes; which is incomplete and imperfect for the detection theory of nonparametric detectors. The analytic expression of the false alarm rate Pfa for the RS nonparametric detector at clutter edges is given in this paper, and the ability of the RS nonparametric detector to control the rise of the false alarm rate at clutter edges is compared to that of the Cell Averaing (CA) CFAR, the Greatest Of (GO) CFAR and the Ordered Statistic (OS) CFAR with incoherent integration. When both of the heavy and the weak clutters follow a Rayleigh distribution, it is shown that the rise of the false alarm rate for the RS detector at clutter edges lies between that of the CA-CFAR and that of the OS-CFAR with incoherent integration. If a non-Gaussian distributed clutter with a long tail moves into the reference window, the rise of the CA-CFAR, the GO-CFAR and the OS-CFAR with incoherent integration reaches a peak of more than 3 orders of magnitude, and can not return to the original pre-designed Pfa. However, the RS nonparametric detector exhibits its inherent advantage in such situation, it can maintain a constant false alarm rate even the distribution form of clutter becomes a different one.
The performance of a Constant False Alarm Rate (CFAR) detector is often evaluated in three typical backgrounds - homogeneous environment, multiple targets situation and clutter edges described by Prof. Rohling. However, there is a lack of the analytic expression of the false alarm rate for the Rank Sum (RS) nonparametric detector at clutter boundaries, and lack of a comparison of the ability for the RS detector to control the rise of the false alarm rate at clutter edges to that of the conventional parametric CFAR schemes; which is incomplete and imperfect for the detection theory of nonparametric detectors. The analytic expression of the false alarm rate Pfa for the RS nonparametric detector at clutter edges is given in this paper, and the ability of the RS nonparametric detector to control the rise of the false alarm rate at clutter edges is compared to that of the Cell Averaing (CA) CFAR, the Greatest Of (GO) CFAR and the Ordered Statistic (OS) CFAR with incoherent integration. When both of the heavy and the weak clutters follow a Rayleigh distribution, it is shown that the rise of the false alarm rate for the RS detector at clutter edges lies between that of the CA-CFAR and that of the OS-CFAR with incoherent integration. If a non-Gaussian distributed clutter with a long tail moves into the reference window, the rise of the CA-CFAR, the GO-CFAR and the OS-CFAR with incoherent integration reaches a peak of more than 3 orders of magnitude, and can not return to the original pre-designed Pfa. However, the RS nonparametric detector exhibits its inherent advantage in such situation, it can maintain a constant false alarm rate even the distribution form of clutter becomes a different one.
2019, 41(12): 2865-2872.
doi: 10.11999/JEIT180933
Abstract:
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
2019, 41(12): 2873-2880.
doi: 10.11999/JEIT180953
Abstract:
A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform. Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range Walk Correction (RWC), and solve the mismatch between the range model and the signal after RWC. Then azimuth Doppler resampling is used to correct the SV phase coefficients. The focused image is achieved by SPECAN technique. Finally, the proposed algorithm is validated by processing of simulated SAR data, and has significant improvement on focusing quality over the reference one.
A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform. Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range Walk Correction (RWC), and solve the mismatch between the range model and the signal after RWC. Then azimuth Doppler resampling is used to correct the SV phase coefficients. The focused image is achieved by SPECAN technique. Finally, the proposed algorithm is validated by processing of simulated SAR data, and has significant improvement on focusing quality over the reference one.
2019, 41(12): 2881-2888.
doi: 10.11999/JEIT190108
Abstract:
The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters (\begin{document}$ H/{\alpha} $\end{document} ![]()
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The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters (
A Novel Micro-motion Multi-target Wideband Resolution Algorithm Based on Curve Overlap Extrapolation
2019, 41(12): 2889-2895.
doi: 10.11999/JEIT190033
Abstract:
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
2019, 41(12): 2896-2902.
doi: 10.11999/JEIT181152
Abstract:
In the system of distributed radar array system using phase interference angle measurement, the phase center coordinate error of arrays and the phase difference error have relatively large influence on the angle measurement. The phase center position is often inconsistent with physical center position. Thus it is necessary to compensate these errors precisely. Far field radiation sources are often used to calibrate radar in traditional calibration methods. However, it is usually hard to achieve far field radiation sources for distributed radar array with large space between units surveilling space targets. In this paper, a calibration method based on the precise ephemeris of refined orbit satellites without measuring with special instruments is proposed. The phase error caused by coordinate error can be whitened by the precise ephemeris of multiple arcs, and the coordinate and phase difference will be searched out by matching the minimum variance. This method can get the errors easily. The simulation results and actual data verify that angle measurement accuracy gets large improvement by the method.
In the system of distributed radar array system using phase interference angle measurement, the phase center coordinate error of arrays and the phase difference error have relatively large influence on the angle measurement. The phase center position is often inconsistent with physical center position. Thus it is necessary to compensate these errors precisely. Far field radiation sources are often used to calibrate radar in traditional calibration methods. However, it is usually hard to achieve far field radiation sources for distributed radar array with large space between units surveilling space targets. In this paper, a calibration method based on the precise ephemeris of refined orbit satellites without measuring with special instruments is proposed. The phase error caused by coordinate error can be whitened by the precise ephemeris of multiple arcs, and the coordinate and phase difference will be searched out by matching the minimum variance. This method can get the errors easily. The simulation results and actual data verify that angle measurement accuracy gets large improvement by the method.
2019, 41(12): 2903-2910.
doi: 10.11999/JEIT181102
Abstract:
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
2019, 41(12): 2911-2918.
doi: 10.11999/JEIT190142
Abstract:
In order to satisfy the requirement of elliptical beam antenna with low profile, a novel design technique of the hybrid-structural antenna with elliptical beam is proposed. The hybrid-structural antenna consists of the ring-focus elliptical antenna in inner-ring region and the Cassegrain elliptical antenna in outer-ring region. The design method, procedure and shaping formula are presented in detail. A 600 mm×1200 mm reflector antenna is designed and its tolerance analysis is also given. The results show that the novel structural antenna can operate in Ku/Ka dual bands, antenna efficiency is greater than 56% and Voltage Standing Wave Ratio (VSWR) is better than 1.27, and its side lobe levels in the EL and AZ planes are below –12.2 dB and –14.6 dB respectively. The simulated results of Grasp and CST software agree well, which verify the effectiveness of the design method.
In order to satisfy the requirement of elliptical beam antenna with low profile, a novel design technique of the hybrid-structural antenna with elliptical beam is proposed. The hybrid-structural antenna consists of the ring-focus elliptical antenna in inner-ring region and the Cassegrain elliptical antenna in outer-ring region. The design method, procedure and shaping formula are presented in detail. A 600 mm×1200 mm reflector antenna is designed and its tolerance analysis is also given. The results show that the novel structural antenna can operate in Ku/Ka dual bands, antenna efficiency is greater than 56% and Voltage Standing Wave Ratio (VSWR) is better than 1.27, and its side lobe levels in the EL and AZ planes are below –12.2 dB and –14.6 dB respectively. The simulated results of Grasp and CST software agree well, which verify the effectiveness of the design method.
2019, 41(12): 2919-2924.
doi: 10.11999/JEIT181145
Abstract:
Based on the beam wave synchronization interaction in transverse and longitudinal directions at the same time and derived from Maxwell’s equation and linear Vlasov equation, the planar metallic grating beam-wave interaction " hot” dispersion equation considering both cyclotron resonance and Cherenkov resonance is deduced. Through the reasonable selection for geometric and electrical parameters, the numerical calculation and analysis of the " hot” dispersion equation show that the beam-wave interaction gain and frequency band with the cyclotron resonance enhancement effect are higher than those with only Cherenkov resonance radiation.
Based on the beam wave synchronization interaction in transverse and longitudinal directions at the same time and derived from Maxwell’s equation and linear Vlasov equation, the planar metallic grating beam-wave interaction " hot” dispersion equation considering both cyclotron resonance and Cherenkov resonance is deduced. Through the reasonable selection for geometric and electrical parameters, the numerical calculation and analysis of the " hot” dispersion equation show that the beam-wave interaction gain and frequency band with the cyclotron resonance enhancement effect are higher than those with only Cherenkov resonance radiation.
2019, 41(12): 2925-2931.
doi: 10.11999/JEIT181049
Abstract:
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2～13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2～13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
2019, 41(12): 2932-2938.
doi: 10.11999/JEIT181127
Abstract:
The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, the selector is emploied to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system maps the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, the selector is emploied to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system maps the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
2019, 41(12): 2939-2944.
doi: 10.11999/JEIT181125
Abstract:
Penalized programs are widely used to solve linear inverse problems in the presence of noise. For now, the study of the performance of panelized programs has two disadvantages. First, the results have some limitations on the tradeoff parameters. Second, the effect of the direction of the noise is not clear. This paper studies the performance of penalized programs when bounded noise is presented. A geometry condition which is used to study the noise-free problems and constrained problems is provided. Under this condition, an explicit error bound which guarantees stable recovery (i.e., the recovery error is bounded by the observation noise up to some constant factor) is proposed. The results are different from many previous studies in two folds. First, the results provide an explicit bound for all positive tradeoff parameters, while many previous studies require that the tradeoff parameter is sufficiently large. Second, the results clear the role of the direction of the observation noise playing in the recovery error, and reveal the relationship between the optimal tradeoff parameters and the noise direction. Furthermore, if the sensing matrix has independent standard normal entries, the above geometry condition can be studied using Gaussian process theory, and the measurement number needed to guarantee stable recovery with high probability is obtained. Simulations are provided to verify the theoretical results.
Penalized programs are widely used to solve linear inverse problems in the presence of noise. For now, the study of the performance of panelized programs has two disadvantages. First, the results have some limitations on the tradeoff parameters. Second, the effect of the direction of the noise is not clear. This paper studies the performance of penalized programs when bounded noise is presented. A geometry condition which is used to study the noise-free problems and constrained problems is provided. Under this condition, an explicit error bound which guarantees stable recovery (i.e., the recovery error is bounded by the observation noise up to some constant factor) is proposed. The results are different from many previous studies in two folds. First, the results provide an explicit bound for all positive tradeoff parameters, while many previous studies require that the tradeoff parameter is sufficiently large. Second, the results clear the role of the direction of the observation noise playing in the recovery error, and reveal the relationship between the optimal tradeoff parameters and the noise direction. Furthermore, if the sensing matrix has independent standard normal entries, the above geometry condition can be studied using Gaussian process theory, and the measurement number needed to guarantee stable recovery with high probability is obtained. Simulations are provided to verify the theoretical results.
2019, 41(12): 2945-2950.
doi: 10.11999/JEIT190012
Abstract:
To solve the problem of weak signals detection in non-Gaussian background, a method based on Sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
To solve the problem of weak signals detection in non-Gaussian background, a method based on Sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
2019, 41(12): 2951-2956.
doi: 10.11999/JEIT190067
Abstract:
The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter increases significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.
The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter increases significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.
2019, 41(12): 2957-2964.
doi: 10.11999/JEIT190144
Abstract:
Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter Marginalized Particle Filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR (MPF-VBM-Corrlation) have the superior performances to the compared ones.
Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter Marginalized Particle Filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR (MPF-VBM-Corrlation) have the superior performances to the compared ones.
2019, 41(12): 2965-2971.
doi: 10.11999/JEIT180947
Abstract:
The transient signal without modulation information of the radiation source can characterize the unintentional modulation characteristics of the radiation source. The analysis of the transient signal can realize the radiation source identification. In the switching on and frequency conversion process of the frequency-hopping signal, there is a transient adjustment time without information transmission. In the transient adjustment moment, the signal transmitted by the transmitter is a non-linear, non-stationary and non-Gaussian signal without modulation information. This transient time series can reflect the device characteristics of the frequency-hopping transmitter, and the sequence often exhibits complex chaotic characteristics. Therefore, from the idea of chaotic time series analysis and Low-rank characteristics of transient signal, a frequency-hopping transmitter classification algorithm is proposed based on chaotic attractor reconstruction and Low-rank clustering. The experimental tests show that the transient signal of the frequency-hopping transmitter belongs to the chaotic time series. At the same time, the classification results of the frequency-hopping signals demonstrate the feasibility of the Low-rank clustering algorithm in frequency-hopping transmitter classification.
The transient signal without modulation information of the radiation source can characterize the unintentional modulation characteristics of the radiation source. The analysis of the transient signal can realize the radiation source identification. In the switching on and frequency conversion process of the frequency-hopping signal, there is a transient adjustment time without information transmission. In the transient adjustment moment, the signal transmitted by the transmitter is a non-linear, non-stationary and non-Gaussian signal without modulation information. This transient time series can reflect the device characteristics of the frequency-hopping transmitter, and the sequence often exhibits complex chaotic characteristics. Therefore, from the idea of chaotic time series analysis and Low-rank characteristics of transient signal, a frequency-hopping transmitter classification algorithm is proposed based on chaotic attractor reconstruction and Low-rank clustering. The experimental tests show that the transient signal of the frequency-hopping transmitter belongs to the chaotic time series. At the same time, the classification results of the frequency-hopping signals demonstrate the feasibility of the Low-rank clustering algorithm in frequency-hopping transmitter classification.
2019, 41(12): 2972-2979.
doi: 10.11999/JEIT190032
Abstract:
In order to enhance the useful information in the image and improve the visual effect of the image, a Non-local Multi-scale Fractional Differential(NMFD) image enhancement operator is proposed. The operator divides the image into several sub-images and calculates the edge intensity coefficient, entropy value and roughness of each sub-image, and the obtained feature data are normalized in a unified scale in the global image range. Then, the normalized data are weighted to be the non-local eigenvalues of the image. Finally, an exponential function is used to establish the non-linear quantization relationship between image detail features and the value of fractional order. Thus, the fractional order of different scales can be determined in different image sub-block regions, so that the non-local multi-scale image enhancement model is realized.
In order to enhance the useful information in the image and improve the visual effect of the image, a Non-local Multi-scale Fractional Differential(NMFD) image enhancement operator is proposed. The operator divides the image into several sub-images and calculates the edge intensity coefficient, entropy value and roughness of each sub-image, and the obtained feature data are normalized in a unified scale in the global image range. Then, the normalized data are weighted to be the non-local eigenvalues of the image. Finally, an exponential function is used to establish the non-linear quantization relationship between image detail features and the value of fractional order. Thus, the fractional order of different scales can be determined in different image sub-block regions, so that the non-local multi-scale image enhancement model is realized.
2019, 41(12): 2980-2986.
doi: 10.11999/JEIT190261
Abstract:
A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with Wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with Wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
2019, 41(12): 2987-2994.
doi: 10.11999/JEIT190043
Abstract:
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
2019, 41(12): 2995-2999.
doi: 10.11999/JEIT180939
Abstract:
Due to the wide applications in association schemes, authentication codes and secret sharing schemes etc., construction of the linear codes with a few weights is an important research topic. A class of linear codes with four-weight and six-weight over finite field\begin{document}${F_p}$\end{document} ![]()
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Due to the wide applications in association schemes, authentication codes and secret sharing schemes etc., construction of the linear codes with a few weights is an important research topic. A class of linear codes with four-weight and six-weight over finite field
2019, 41(12): 3000-3005.
doi: 10.11999/JEIT190071
Abstract:
Families of pseudorandom sequences derived from Euler quotients modulo odd prime power possess sound cryptographic properties. In this paper, according to the theory of residue class ring, a new classes of binary sequences with period\begin{document}$2{p^{m + 1}}$\end{document} ![]()
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Families of pseudorandom sequences derived from Euler quotients modulo odd prime power possess sound cryptographic properties. In this paper, according to the theory of residue class ring, a new classes of binary sequences with period
2019, 41(12): 3006-3013.
doi: 10.11999/JEIT190013
Abstract:
In the Network Function Virtualization (NFV) environment, for the reliability problem of Service Function Chain (SFC) deployment, a joint optimization method is proposed for backup Virtual Network Function (VNF) selection, backup instance placement and service function chain deployment. Firstly, the method defines a virtual network function measurement standard named the unit cost reliability improvement value to improve the backup virtual network function selection method. Secondly, the joint backup mode is used to adjust the placement strategy between adjacent backup instances to reduce bandwidth resources overhead. Finally, the reliability-guarantee problem of the whole service function chain deployment is modeled as integer linear programming, and a heuristic algorithm based on the shortest path is proposed to overcome the complexity of integer linear programming. The simulation results show that the method optimizes resource allocation while prioritizing the network service reliability requirements, and improves the request acceptance rate.
In the Network Function Virtualization (NFV) environment, for the reliability problem of Service Function Chain (SFC) deployment, a joint optimization method is proposed for backup Virtual Network Function (VNF) selection, backup instance placement and service function chain deployment. Firstly, the method defines a virtual network function measurement standard named the unit cost reliability improvement value to improve the backup virtual network function selection method. Secondly, the joint backup mode is used to adjust the placement strategy between adjacent backup instances to reduce bandwidth resources overhead. Finally, the reliability-guarantee problem of the whole service function chain deployment is modeled as integer linear programming, and a heuristic algorithm based on the shortest path is proposed to overcome the complexity of integer linear programming. The simulation results show that the method optimizes resource allocation while prioritizing the network service reliability requirements, and improves the request acceptance rate.
2019, 41(12): 3014-3021.
doi: 10.11999/JEIT190016
Abstract:
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
2019, 41(12): 3022-3028.
doi: 10.11999/JEIT181101
Abstract:
A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN). The algorithm includes network partitioning and distributed algorithm. Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently. Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions. Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms of accuracy in large scale network, which can meet the needs of nodes localization in large scale network.
A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN). The algorithm includes network partitioning and distributed algorithm. Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently. Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions. Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms of accuracy in large scale network, which can meet the needs of nodes localization in large scale network.
2019, 41(12): 3029-3035.
doi: 10.11999/JEIT190168
Abstract:
Considering the problem of scattered node mapping and more hops of link mapping in the traditional virtual network energy-saving embedding, the node and link are mapped simultaneously by using the minimum spanning tree topology of the virtual network request, and Energy-saving Virtual Network Embedding algorithm based on Sliding Region Particle Swarm (EVNE_SRPS) is proposed. When a virtual network request arrives, the minimum spanning tree topology is generated, the root node is the node with the shortest path length; Multiple regions are randomly selected as the particle object in the substrate network, and the minimum spanning tree topology of the virtual network request is mapped in the regional center; The fitness of the particles is calculated. The optimal solution of the group and the individual is finded, and the sliding direction and the location of the update region under the guidance of the optimal solution are determined. After the iteration, the mapping scheme of the virtual network is obtained. The experimental results show that compared with the existing algorithms, the network energy consumption is reduced, and the internet service providers revenue to cost ratio is improved.
Considering the problem of scattered node mapping and more hops of link mapping in the traditional virtual network energy-saving embedding, the node and link are mapped simultaneously by using the minimum spanning tree topology of the virtual network request, and Energy-saving Virtual Network Embedding algorithm based on Sliding Region Particle Swarm (EVNE_SRPS) is proposed. When a virtual network request arrives, the minimum spanning tree topology is generated, the root node is the node with the shortest path length; Multiple regions are randomly selected as the particle object in the substrate network, and the minimum spanning tree topology of the virtual network request is mapped in the regional center; The fitness of the particles is calculated. The optimal solution of the group and the individual is finded, and the sliding direction and the location of the update region under the guidance of the optimal solution are determined. After the iteration, the mapping scheme of the virtual network is obtained. The experimental results show that compared with the existing algorithms, the network energy consumption is reduced, and the internet service providers revenue to cost ratio is improved.
2019, 41(12): 3036-3042.
doi: 10.11999/JEIT181169
Abstract:
Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility. A dynamic flow aggregation method to overcome present problems is proposed. An Enhanced Rough K-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible. Internet scheduler experiment is carried out and a comparison is made with existing methods. Experimental results suggest that proposed method has advantages not only on flexibility of aggregation, but also on assurance of QoS of Internet flows. In addition, the consistency of QoS allocation under different network environment is investigated.
Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility. A dynamic flow aggregation method to overcome present problems is proposed. An Enhanced Rough K-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible. Internet scheduler experiment is carried out and a comparison is made with existing methods. Experimental results suggest that proposed method has advantages not only on flexibility of aggregation, but also on assurance of QoS of Internet flows. In addition, the consistency of QoS allocation under different network environment is investigated.
2019, 41(12): 3043-3050.
doi: 10.11999/JEIT190004
Abstract:
Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.
Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.
