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GS-orthogonalization OMP method for space target detection via bistatic space-based radar
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作者 Shuyu ZHENG Libing JIANG +2 位作者 Qingwei YANG Yingjian ZHAO Zhuang WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第7期333-351,共19页
A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and ... A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system.However,the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets.To be specific,it will encounter the problems of Range Cell Migration(RCM)and Doppler Frequency Migration(DFM),which degrade the long-time coherent integration performance for target detection and localization inevitably.To solve this problem,a novel target detection method based on an improved Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm is proposed in this paper.First,the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed.Then,the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets.Thereafter,the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters.Finally,coherent integration can be achieved by performing the Fast Fourier Transform(FFT)operation along the slow time direction on compensated echo signal.Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection. 展开更多
关键词 Bistatic space-based radar High-speed maneuvering space targets detection Range Cell Migration(RCM) Doppler Frequency Migration(DFM) Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm
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Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT
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作者 CHEN Xiaolong GUAN Jian +2 位作者 ZHENG Jibin ZHANG Yue YU Xiaohan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第5期1116-1129,共14页
Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous... Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter back-ground,and at the same time improving the calculation efficiency has become an urgent problem to be solved.The sparse transformation theory is introduced to LTCI in this paper,and a non-parametric searching sparse LTCI(SLTCI)based maneuvering target detection method is proposed.This method performs time reversal(TR)and second-order Keystone transform(SKT)in the range frequency&slow-time data to complete high-order range walk compensation,and achieves the coherent integra-tion of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform(RSFRFT).It can compensate for the nonlinear range migration caused by high-order motion.S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method,which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods. 展开更多
关键词 radar maneuvering target detection sea clutter long-time coherent integration(LTCI) sparse transformation time reversal(TR) second-order Keystone transform(SKT)
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