多测量向量稀疏度自适应算法实现了MIMO稀疏线性阵列的非正交波形分离

Qiao Chen, N. Tong, Yibei Dong, Lei Bao, Shuai Chen, Lixun Han
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引用次数: 0

摘要

为了解决压缩感知波形分离方法中存在的不规则交叉距离偏移(IRCM)问题,本文研究了MIMO雷达接收信号的特性,探讨了不同观测通道中多个测量向量的联合稀疏性。采用多测量向量稀疏恢复算法代替单观测向量恢复算法分离回波。它不仅可以高精度地重建一维距离图像,而且可以抑制IRCM。针对现有联合稀疏恢复算法由于稀疏度未知导致分离效果不佳的问题,提出了一种稀疏度自适应联合稀疏恢复算法,以提高波形分离效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Measurement Vector Sparsity Adaptive Algorithm Realize the non-orthogonal Waveform Separation of MIMO Sparse Linear Array
In order to solve the irregular crossing distance migration (IRCM) in the compression sensing waveform separation method, the paper studies the characteristics of the signal received by MIMO radar and explores the joint sparsity of the multiple measurement vector in different observation channels. Multiple measurement vector sparse recovery algorithm is used to separate the echo replaced the single-observation vector restoration algorithm. It cannot only reconstruct the one-dimensional distance image with high precision but also suppress the IRCM. Aiming at the problem that current joint sparse recovery algorithm has poor separation effect due to the unknown sparsity, a sparse degree adaptive joint sparse recovery algorithm is proposed to improve the effect of waveform separation.
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