基于混沌随机滤波器的CS-MIMO雷达测量矩阵优化设计

提出了一种在压缩感知多输入多输出（compressive sensing-multiple input multiple output,CS-MIMO）雷达中利用混沌非线性系统设计随机滤波器进而实现测量矩阵优化的方法。目前，大部分研究采用高斯随机矩阵作为测量矩阵，这类测量矩阵的局限性是，每次仿真实验产生的矩阵互不相同，雷达系统无法实现在线优化，且其对硬件要〖JP〗求高,实现困难。在CS-MIMO雷达信号模型基础上构造稀疏基，提出了基于随机滤波器结构的测量矩阵设计方法，利用混沌序列构造随机滤波器系数，完成对雷达回波的压缩观测。同时以Gram矩阵逼近对角矩阵为准则对随机滤波等效测量矩阵进行优化，进一步提高雷达系统性能。仿真结果表明所提出的基于混沌随机滤波器的CS-MIMO雷达测量矩阵设计与优化算法能够有效提高波达角(direction of arrival, DOA)估计精度。

Measurement matrix optimization for CS-MIMO radar based on chaotic random filter

An optimized measurement matrix design method for compressive sensing-multiple input multiple output (CS-MIMO) radar is proposed by applying the chaotic dynamical system to random filter design. Most of the previous research takes the Gaussian random matrix as the measurement matrix. However, it cannot realize on line optimization and is hard to be implemented in physical electric circuit. Considering that the basis matrix is obtained from the CS-MIMO radar signal model, we propose a new measurement matrix design method applying the random filter. By constructing the filter coefficients with the chaotic sequence, the CS is achieved for the received signal. Moreover, an optimization method is performed on the equivalent measurement matrix of the random filter, by making the Gram matrix approach the diagonal matrix. The simulation results show that the proposed measurement matrix design and optimization method based on the chaotic random filter can effectively improve the direction of arrival (DOA) estimation accuracy of the CS-MIMO radar.