Power Allocation and Measurement Matrix Design for Block CS-Based Distributed MIMO Radars
Power Allocation and Measurement Matrix Design for Block CS-Based Distributed MIMO Radars
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DOI：
10.1016/j.ast.2016.03.005
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Azra Abtahi;M. Modarres-Hashemi;F. Marvasti;F. Tabataba
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文献类型：
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作者：
Azra Abtahi;M. Modarres-Hashemi;F. Marvasti;F. Tabataba
多输入多输出（MIMO）雷达提供更高的分辨率，更好的目标检测，更准确的目标参数估计。由于目标在空间速度域的稀疏性，当采样率远小于奈奎斯特采样率时，可以利用压缩感知来提高MIMO雷达的性能。在分布式MIMO雷达中，由于接收信号是一个基上的块稀疏信号，因此可以用块压缩感知方法代替经典的压缩感知方法，以获得更好的性能。针对基于分组CS的分布式MIMO雷达，提出了两种提高雷达性能的新方法。第一种方法是一种新的能量分配方法，另一种方法是一种新的测量矩阵的优化设计方法。这些方法基于最小化感测矩阵块的块相干性之和的上界。仿真结果表明，这两种方法提高了多目标参数估计的精度。
Multiple-input multiple-output (MIMO) radars offer higher resolution, better target detection, and more accurate target parameter estimation. Due to the sparsity of the targets in space-velocity domain, we can exploit Compressive Sensing (CS) to improve the performance of MIMO radars when the sampling rate is much less than the Nyquist rate. In distributed MIMO radars, block CS methods can be used instead of classical CS ones for more performance improvement, because the received signal in this group of MIMO radars is a block sparse signal in a basis. In this paper, two new methods are proposed to improve the performance of the block CS-based distributed MIMO radars. The first one is a new method for optimal energy allocation to the transmitters, and the other one is a new method for optimal design of the measurement matrix. These methods are based on minimizing an upper bound of the sum of the block-coherences of the sensing matrix blocks. Simulation results show an increase in the accuracy of multiple targets parameters estimation for both proposed methods.