基于稀疏超分辨的机载TS-MIMO雷达慢速运动目标检测方法研究
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摘要
相比于传统机载相控阵雷达,机载发射子孔径-多输入多输出(TS-MIMO)雷达的空域自由度成倍扩大,采用空时自适应处理(STAP)时所需训练样本也显著增长,因此性能在实际非均匀杂波环境下急剧下降,导致慢速运动目标无法检测。不同于传统STAP方法,提出了一种基于空时二维稀疏超分辨谱估计的慢速运动目标检测方法。该方法采用稀疏贝叶斯学习算法直接对待检测距离门数据进行空时二维谱估计,然后再基于雷达先验参数将空时二维超分辨谱中主要杂波分量置零,最后在角-多普勒域进行常规恒虚警处理的检测目标。所提方法无需训练样本,因此可显著提升机载TS-MIMO雷达在实际应用中的慢速运动目标检测能力。仿真实验验证了所提方法的有效性。
Compared with traditional airborne phased array radar, the spatial-domain degrees-of-freedom of the airborne Transmit Subaperturing-Multiple Input Multiple Output(TS-MIMO) radar are multiplied, and the training samples required for Space-Time Adaptive Processing(STAP) are also significantly increased, so the performance degrades sharply in the actual non-uniform clutter environment, and slow moving targets cannot be detected. Different from the traditional STAP method, this paper proposes a slow moving target detection method based on two-dimensional space-time sparse super-resolution spectrum estimation. The method uses the sparse Bayesian learning algorithm to directly measure the range cell data for space-time spectrum estimation. Then, the main clutter component in the space-time super-resolution spectrum is set to zero based on the radar prior parameters, and finally the slow target can be detected in the angle-Doppler domain based onconventional constant false-alarm processing. The proposed method does not require training samples, so it can significantly improve the slow moving target detection capability of the onboard TS-MIMO radar in practical applications. Simulation experiments verify the effectiveness of the proposed method.
Compared with traditional airborne phased array radar, the spatial-domain degrees-of-freedom of the airborne Transmit Subaperturing-Multiple Input Multiple Output(TS-MIMO) radar are multiplied, and the training samples required for Space-Time Adaptive Processing(STAP) are also significantly increased, so the performance degrades sharply in the actual non-uniform clutter environment, and slow moving targets cannot be detected. Different from the traditional STAP method, this paper proposes a slow moving target detection method based on two-dimensional space-time sparse super-resolution spectrum estimation. The method uses the sparse Bayesian learning algorithm to directly measure the range cell data for space-time spectrum estimation. Then, the main clutter component in the space-time super-resolution spectrum is set to zero based on the radar prior parameters, and finally the slow target can be detected in the angle-Doppler domain based onconventional constant false-alarm processing. The proposed method does not require training samples, so it can significantly improve the slow moving target detection capability of the onboard TS-MIMO radar in practical applications. Simulation experiments verify the effectiveness of the proposed method.
引文
[1] BLISS D W,FORSYTHE K W.Multiple-input multiple-output (MIMO) radar and imaging:degrees of freedom and re-solution[C]//IEEE 37th Asilomar Conference on Signals,Systems and Computers,Pacific Grove,2003:54-59.
[2] RABIDEAU D J,PARKER P.Ubiquitous MIMO multifunction digital array radar[C]//IEEE 37th Asilomar Confe-rence on Signals,Systems and Computers,Pacific Grove, CA,2003:1057-1064.
[3] 夏铭钰,李俊杰,王勇.相控阵雷达信息在舰炮武器系统中优化使用[J].火力与指挥控制,2016,41(1):85- 87.
[4] KANTOR J M,DAVIS S K.Airborne MIMO GMTI radar[R].[S.l.].Technical Report,MIT Lincoln Laboratory, 2011.
[5] DAUM F,HUANG J.MIMO radar:snake oil or good idea?[J].Aerospace & Electronic Systems Magazine,2009,24(5):8-12.
[6] FUHRMANN D R,BROWNING J P,RANGASWAMY M.Signaling strategies for the hybrid MIMO phased-array radar[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(1):66-78.
[7] LI H B,HIMED B.Transmit subaperturing for MIMO radar with co-located antennas[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(1):55-65.
[8] HASSANIEN A,VOROBYOV S A.Phased-MIMO radar:a tradeoff between phased-array and MIMO radars[J].IEEE Transactions on Signal Processing,2010,58(6):3137-3151.
[9] BRENNAN L E,MALLETT J D,REED I S.Theory of adaptive radar[J].IEEE Transactions on Aerospace and Electronics Systems,1973,9(2):237- 251.
[10] CHEN C Y,VAIDYANATHAN P P.MIMO radar space-time adaptive processing using prolate spheroidal wave functions[J].IEEE Transactions on Signal Processing, 2008,56(2):623- 635.
[11] WANG G H,LU Y L.Clutter rank of STAP in MIMO radar with waveform diversity[J].IEEE Transactions on Signal Processing,2010,58(2):938-943.
[12] SELESNICH I W,PILLAI S U,LI K Y,et al.Angle-Doppler processing using sparse regularization[C]//IEEE International Conference on Acoustics,Speech and Signal Processing,Dallas,2010:2750- 2753.
[13] LI J,ZHU X,STOICA P,et al.High resolution angle-Doppler imaging for MTI radar[J].IEEE Transactions on Aerospace and Electronic Systems, 2010,46(3):1544-1556.
[14] TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal on Machine Learning, 2001,1(2):211- 244.
[15] WIPF D P,RAO B D.An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J].IEEE Transactions on Signal Processing,2007,55(7):3704-3716.
[16] TROPP J A.Algorithms for simultaneous sparse approximation.Part II:convex relaxation[J].Signal Processing, 2006,86(3):589-602.
[17] COTTER S F,RAO B D,ENGAN K,et al.Sparse solutions to linear inverse problems with multiple measurement vectors[J].IEEE Transactions on Signal Processing,2005,53(7):2477- 2488.
[18] DAVIES M E,ELDAR Y C.Rank awareness in joint sparse recovery[J].IEEE Transactions on Information Theory, 2012,58(2):1135-1146.
[19] DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society.Series B (Methodological),1977,39(1):1- 38.
[2] RABIDEAU D J,PARKER P.Ubiquitous MIMO multifunction digital array radar[C]//IEEE 37th Asilomar Confe-rence on Signals,Systems and Computers,Pacific Grove, CA,2003:1057-1064.
[3] 夏铭钰,李俊杰,王勇.相控阵雷达信息在舰炮武器系统中优化使用[J].火力与指挥控制,2016,41(1):85- 87.
[4] KANTOR J M,DAVIS S K.Airborne MIMO GMTI radar[R].[S.l.].Technical Report,MIT Lincoln Laboratory, 2011.
[5] DAUM F,HUANG J.MIMO radar:snake oil or good idea?[J].Aerospace & Electronic Systems Magazine,2009,24(5):8-12.
[6] FUHRMANN D R,BROWNING J P,RANGASWAMY M.Signaling strategies for the hybrid MIMO phased-array radar[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(1):66-78.
[7] LI H B,HIMED B.Transmit subaperturing for MIMO radar with co-located antennas[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(1):55-65.
[8] HASSANIEN A,VOROBYOV S A.Phased-MIMO radar:a tradeoff between phased-array and MIMO radars[J].IEEE Transactions on Signal Processing,2010,58(6):3137-3151.
[9] BRENNAN L E,MALLETT J D,REED I S.Theory of adaptive radar[J].IEEE Transactions on Aerospace and Electronics Systems,1973,9(2):237- 251.
[10] CHEN C Y,VAIDYANATHAN P P.MIMO radar space-time adaptive processing using prolate spheroidal wave functions[J].IEEE Transactions on Signal Processing, 2008,56(2):623- 635.
[11] WANG G H,LU Y L.Clutter rank of STAP in MIMO radar with waveform diversity[J].IEEE Transactions on Signal Processing,2010,58(2):938-943.
[12] SELESNICH I W,PILLAI S U,LI K Y,et al.Angle-Doppler processing using sparse regularization[C]//IEEE International Conference on Acoustics,Speech and Signal Processing,Dallas,2010:2750- 2753.
[13] LI J,ZHU X,STOICA P,et al.High resolution angle-Doppler imaging for MTI radar[J].IEEE Transactions on Aerospace and Electronic Systems, 2010,46(3):1544-1556.
[14] TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal on Machine Learning, 2001,1(2):211- 244.
[15] WIPF D P,RAO B D.An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J].IEEE Transactions on Signal Processing,2007,55(7):3704-3716.
[16] TROPP J A.Algorithms for simultaneous sparse approximation.Part II:convex relaxation[J].Signal Processing, 2006,86(3):589-602.
[17] COTTER S F,RAO B D,ENGAN K,et al.Sparse solutions to linear inverse problems with multiple measurement vectors[J].IEEE Transactions on Signal Processing,2005,53(7):2477- 2488.
[18] DAVIES M E,ELDAR Y C.Rank awareness in joint sparse recovery[J].IEEE Transactions on Information Theory, 2012,58(2):1135-1146.
[19] DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society.Series B (Methodological),1977,39(1):1- 38.