Existing greedy reconstruction algorithms require signal sparsity, and the remaining sparsity adaptive algorithms can be reconstructed but cannot achieve accurate sparsity estimation. To address this problem, a blind sparsity reconstruction algorithm is proposed in this paper, which is applied to compressed sensing radar receiver system. The proposed algorithm can realize the estimation of signal sparsity and channel position estimation, which mainly consists of two parts. The first part is to use fast search based on dichotomy search, which is based on the high probability reconstruction of greedy algorithm, and uses dichotomy search to cover the number of sparsity. The second part is the signal matching and tracking algorithm, which is mainly used to judge the signal position and reconstruct the signal. Combine the two parts together to realize the blind estimation of the sparsity and the accurate estimation of the number of signals when the number of signals is unknown. The experimental analyses are carried out to evaluate the performance of the reconstruction probability, the accuracy of sparsity estimation, the running time of the algorithm, and the signal-to-noise ratio.

中文翻译：

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基于压缩感知宽带接收机的自适应重建算法

现有的贪婪重构算法需要信号稀疏性，并且剩余的稀疏性自适应算法可以重构，但是不能实现准确的稀疏性估计。针对这一问题，提出了一种盲稀疏重建算法，将其应用于压缩感知雷达接收机系统。该算法可以实现信号稀疏度估计和信道位置估计，主要由两部分组成。第一部分是基于二分法搜索的快速搜索，它基于贪婪算法的高概率重构，并使用二分法搜索来覆盖稀疏性。第二部分是信号匹配与跟踪算法，主要用于判断信号位置和重构信号。当信号数量未知时，将这两部分结合在一起，可以实现稀疏性的盲估计和信号数量的准确估计。进行了实验分析，以评估重建概率的性能，稀疏性估计的准确性，算法的运行时间以及信噪比。