This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.

中文翻译：

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协同定位 MIMO 雷达的压缩域检测和估计

本文提出了一种压缩域信号处理 (CSP) 多输入多输出 (MIMO) 雷达，这是一种 MIMO 雷达方法，通过利用 CSP 的思想实现了大幅降低样本复杂度。CSP MIMO 雷达涉及两个级别的数据压缩，然后在压缩域进行目标检测。首先，在接收天线上应用压缩传感，然后是 Capon 波束成形器，旨在抑制杂波。利用波束形成器输出的稀疏特性，对过滤后的数据应用二次压缩。随后通过在离散化角度空间的每个网格点处制定和解决假设检验问题来进行目标检测。与传统的压缩感知 (CS) MIMO 雷达相比，所提出的方法可以将某些设置中的样本复杂性降低八倍，从而实现更快的目标检测。提供了所提出的检测器的接收器操作特性曲线。仿真结果表明，所提出的方法优于基于恢复的 CS 算法。