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Radar Target Detection via GAMP: A Sparse Recovery Strategy Off the Grid
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2021-04-09 , DOI: 10.1109/tvt.2021.3072227
Benzhou Jin , Fuhui Zhou , Xiaofei Zhang , Qihui Wu , Naofal Al-Dhahir

Sparse recovery (SR) is a promising tool for radar signal processing. However, SR-based target detection is still an open problem in the challenging scenarios of grid mismatch, very high dimensionality and constant false-alarm rate (CFAR) requirement. To address the above challenges, an efficient approach termed as knowledge-aided generalized approximate message passing (KA-GAMP) is proposed. Firstly, traditional signal processing (TSP) is performed to obtain prior knowledge about targets of interest. Based on this prior knowledge, dimensionality reduction is carried out, and a new approximate observation model of the received signal is established. Then, considering the grid-mismatch problem, target parameter estimations are carried out before SR, and an estimate of the measurement matrix is obtained. Finally, by exploiting the sparsity of the received signal, GAMP is adopted to perform target recovery. Based on recovery results, target detection is implemented. Interestingly, it is shown that the noise envelope outputted by GAMP approximately follows an i.i.d Gaussian distribution, and the proposed detector is CFAR. Numerical results via both Monte Carlo simulations and the measured data show that the proposed approach is superior to the TSP-based method in terms of target detection performance.

中文翻译:

通过 GAMP 进行雷达目标检测:离网的稀疏恢复策略

稀疏恢复 (SR) 是一种很有前途的雷达信号处理工具。然而,在网格不匹配、非常高的维度和恒定误报率 (CFAR) 要求等具有挑战性的场景中,基于 SR 的目标检测仍然是一个悬而未决的问题。为了解决上述挑战,提出了一种称为知识辅助广义近似消息传递(KA-GAMP)的有效方法。首先,执行传统的信号处理 (TSP) 以获得有关感兴趣目标的先验知识。在此先验知识的基础上,进行降维,建立新的接收信号近似观测模型。然后,考虑网格失配问题,在SR之前进行目标参数估计,得到测量矩阵的估计。最后,利用接收信号的稀疏性,采用GAMP进行目标恢复。根据恢复结果,实现目标检测。有趣的是,结果表明 GAMP 输出的噪声包络近似遵循 iid 高斯分布,并且建议的检测器是 CFAR。通过蒙特卡罗模拟和测量数据的数值结果表明,所提出的方法在目标检测性能方面优于基于 TSP 的方法。
更新日期:2021-06-11
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