多输入多输出雷达恒模稳健波形联合优化算法
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摘要
针对目标冲激响应及杂波冲激响应分布特性先验知识不准确导致的多输入多输出雷达检测性能下降的问题,提出恒模稳健波形与接收机滤波器联合优化算法。将目标冲激响应及杂波冲激响应分布特性先验知识不准确时的优化问题建模为一个极大极小化问题。运用迭代优化算法将联合优化问题分解为两个子优化步骤:将波形固定时的接收机滤波器权值优化问题建模为广义瑞利商模型,求解得到相应的接收机滤波器权值矢量;利用半正定松弛技术对权值固定时的波形优化问题进行求解,获得对应的波形矩阵,并根据得到的波形矩阵,通过高斯随机化的方法获得所需的恒模波形。对所提算法的收敛性进行了证明,仿真结果表明所提算法有效。
Aimed at the problem that the detection performance losses of multi-input multiple-output radar due to the inaccurate prior knowledge of the distribution characteristics of target impulse response and clutter impulse response,a joint optimization algorithm of constant modulus robust waveform and receiver filter was proposed. The optimization problem in which the prior knowledge of the distribution characteristics of target impulse response and clutter impulse response is not accurate was modeled as a maximum minimization problem. During the optimizations,the joint design problem was divided into two steps: the optimization problem of receiver filter weights when the waveform was fixed was modeled as the generalized Rayleigh quotient model,and the corresponding weight vector of the receiver filter was obtained; the waveform optimization problem of fixed weight was solved by using positive semi-definite relaxation technique to obtain the corresponding waveform matrix,and the constant modulus waveform was obtained by Gauss random method according to the waveform matrix obtained. The convergence of the proposed algorithm was proved,and the simulation results show the effectiveness of the proposed algorithm.
Aimed at the problem that the detection performance losses of multi-input multiple-output radar due to the inaccurate prior knowledge of the distribution characteristics of target impulse response and clutter impulse response,a joint optimization algorithm of constant modulus robust waveform and receiver filter was proposed. The optimization problem in which the prior knowledge of the distribution characteristics of target impulse response and clutter impulse response is not accurate was modeled as a maximum minimization problem. During the optimizations,the joint design problem was divided into two steps: the optimization problem of receiver filter weights when the waveform was fixed was modeled as the generalized Rayleigh quotient model,and the corresponding weight vector of the receiver filter was obtained; the waveform optimization problem of fixed weight was solved by using positive semi-definite relaxation technique to obtain the corresponding waveform matrix,and the constant modulus waveform was obtained by Gauss random method according to the waveform matrix obtained. The convergence of the proposed algorithm was proved,and the simulation results show the effectiveness of the proposed algorithm.
引文
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[9] Yu X X,Cui G L,Marco P,et al. Robust constrained waveform design for MIMO radar with uncertain steering vectors[J]. Eurasip Journal on Advances in Signal Processing,2017,2(1):1856-1860.
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[12] Yang Y,Blum R S. MIMO radar waveform design based on mutual information and minimum mean-square error estimation[J]. IEEE Transactions on Aerospace and Electronic Systems,2007,43(1):330-343.
[13]张贤达.矩阵分析与应用[M]. 2版.北京:清华大学出版社,2013:74-76.ZHANG Xianda. Matrix analysis and application[M]. 2nd ed. Beijing:Tsinghua University Press,2013:74-76.(in Chinese)
[2]陈浩文,黎湘,庄钊文.一种新兴的雷达体制——MIMO雷达[J].电子学报,2012,40(6):1190-1198.CHEN Haowen, LI Xiang, ZHUANG Zhaowen. A rising radar system—MIMO radar[J]. Acta Electronica Sinica,2012,40(6):1190-1198.(in Chinese)
[3]许红波,王怀军,陆珉,等.一种新MIMO雷达DOA估计方法[J].国防科技大学学报,2009,31(3):92-96.XU Hongbo, WANG Huaijun, LU Min, et al. A new algorithm on estimation of DOA using MIMO radar[J].Journal of National University of Defense Technology,2009,31(3):92-96.(in Chinese)
[4] Li J,Xu L Z,Stoica P,et al. Range compression and waveform optimization for MIMO radar:a cramer rao bound based study[J]. IEEE Transactions on Signal Processing,2008,56(1):218-232.
[5] Cui G L,Li H B,Rangaswamy M. MIMO radar waveform design with constant modulus and similarity constraints[J].IEEE Transactions on Signal Processing,2014,62(2):343-353.
[6]唐波,张玉,李科,等.杂波中MIMO雷达恒模波形及接收机联合优化算法研究[J].电子学报,2014,42(9):1705-1711.TANG Bo, ZHANG Yu, LI Ke, et al. Joint constantenvelope waveform and receiver design for MIMO radar in the presence of clutter[J]. Acta Electronica Sinica, 2014,42(9):1705-1711.(in Chinese)
[7]申东,张林让,刘昕,等.用混沌序列提高MIMO雷达多目标检测性能[J].系统工程与电子技术,2013,35(6):1188-1192.SHEN Dong,ZHANG Linrang,LIU Xin,et al. Improvement on performance of MIMO radar for multiple targets detection with chaotic sequences[J]. System Engineering and Electronics,2013,35(6):1188-1192.(in Chinese)
[8]张向阳,廖桂生,许京伟,等.非完备目标先验知识多输入多输出雷达稳健波形设计[J].电波科学学报,2014,29(6):1036-1044.ZHANG Xiangyang,LIAO Guisheng,XU Jingwei,et al.Robust waveform design for MIMO radar with imperfect target prior knowledge[J]. Chinese Journal of Radio Science,2014,29(6):1036-1044.(in Chinese)
[9] Yu X X,Cui G L,Marco P,et al. Robust constrained waveform design for MIMO radar with uncertain steering vectors[J]. Eurasip Journal on Advances in Signal Processing,2017,2(1):1856-1860.
[10] Jiu B, Liu H W, Feng D Z, et al. Minimax robust transmission waveform and receiving filter design for extended target detection with imprecise prior knowledge[J]. Signal Processing,2012,92(1):210-218.
[11] Zhu W,Tang J. Robust design of transmit waveform and receive filter for colocated MIMO radar[J]. IEEE Signal Processing Letters,2015,22(11):2112-2116.
[12] Yang Y,Blum R S. MIMO radar waveform design based on mutual information and minimum mean-square error estimation[J]. IEEE Transactions on Aerospace and Electronic Systems,2007,43(1):330-343.
[13]张贤达.矩阵分析与应用[M]. 2版.北京:清华大学出版社,2013:74-76.ZHANG Xianda. Matrix analysis and application[M]. 2nd ed. Beijing:Tsinghua University Press,2013:74-76.(in Chinese)