CS-MIMO雷达稀疏随机阵列优化设计

Di Xu, Gong Zhang, Zhenni Peng
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引用次数: 0

摘要

为了提高基于压缩感知(CS)理论的MIMO雷达的参数估计性能,提出了一种优化CS-MIMO雷达稀疏随机阵列的方法。考虑到高斯随机矩阵等常用测量矩阵在硬件实现上的困难,本文利用稀疏随机阵列与CS之间的内在联系,研究了一种新的测量矩阵构造方法,利用阵列元素的随机性实现压缩测量。将模拟退火技术应用于CS-MIMO雷达的稀疏随机阵列优化中,通过作用于发射阵列和接收阵列的单元位置,降低等效传感矩阵的相干性,提高参数估计性能。仿真结果验证了该方法的有效性。
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Optimization design of CS-MIMO radar sparse random array
To improve the parameter estimation performance of the compressed sensing(CS) theory based MIMO radar, a method of optimizing the sparse random array in CS-MIMO radar is proposed. Considering the difficulty of hardware implementation of the typically used measurement matrix such as Gaussian random matrix, in this paper, we exploit the inner connection between sparse random array and CS to study a new method of measurement matrix construction and make use of the randomness of the array elements to realize compressive measurement. The simulated annealing is applied to the sparse random array optimization in CS-MIMO radar in order to reduce the coherence of the equivalent sensing matrix and improve the parameter estimation performance by acting on the elements' positions of transmitting and receiving arrays. The simulation results verify the effectiveness of the proposed approach.
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