多输入多输出雷达恒模稳健波形联合优化算法
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国家部委基金资助项目(KY16N161)


Joint optimization algorithm of constant modulus robust waveform for multiinput multiple-output radar
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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。

    Abstract:

    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. 

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崔琛,孙从易,郝天铎,等.多输入多输出雷达恒模稳健波形联合优化算法[J].国防科技大学学报,2018,40(4):166-172.
CUI Chen, SUN Congyi, HAO Tianduo, et al. Joint optimization algorithm of constant modulus robust waveform for multiinput multiple-output radar[J]. Journal of National University of Defense Technology,2018,40(4):166-172.

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  • 收稿日期:2017-06-01
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  • 在线发布日期: 2018-09-17
  • 出版日期: 2018-08-28
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