基于感知矩阵统计相关系数最小化的压缩感知雷达波形优化设计

Waveform Design for Compressive Sensing Radar Based on Minimizing the Statistical Coherence of the Sensing Matrix

为了改善压缩感知雷达(Compressive Sensing Radar,CSR)目标参数提取的性能,该文提出一种最小化感知矩阵统计相关系数的CSR波形优化设计方法。文中首先建立了通用的CSR系统模型,推导了最小化感知矩阵统计相关系数的波形优化目标函数,其次以多相编码信号作为优化码型并采用遗传算法对目标函数进行优化求解。优化设计的波形使得感知矩阵子矩阵近似正交程度达到最优,与传统波形相比,能够有效降低目标参数估计误差,提高可检测目标个数的上限,改善了CSR目标参数提取的准确性和鲁棒性。计算机仿真验证了该方法的有效性。

To enhance the performance of Compressive Sensing Radar（CSR） target information extraction ability,a CSR optimal waveform design method based on minimizing the statistical coherence of the sensing matrix is proposed.First,a universal CSR model is established and waveform optimization object function minimizing the coherence of the sensing matrix is derived.Then,the Genetic Algorithm（GA） is employed to solve this problem with polyphase coded signal as an example code.The optimized waveform makes the sub-sensing matrix orthogonality degree approximately optimal.Comparing with traditional waveforms,this waveform reduces effectively the target information estimation error,increases the permissible upper bound of target detection number,and enhances the accuracy and robustness of CSR target information extraction.Computer simulation shows the effectiveness of the method.