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Compressive Sensing for target DOA estimation in radar

机译:雷达目标DOA估计的压缩感知

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Compressive Sensing (CS) is a newly developed technique which can accurately recover signals with only a few random samples, provided that the signals are sparse or compressible in a certain domain. In this paper, we aim to make use of the CS technique to improve the target direction of arrival (DOA) estimation performance in radar. The idea is deploying the limited number of array elements in a much longer aperture (where the inter-element spacing is non-uniform and much greater than half wavelength), and using CS technique to estimate the DOA of targets. Benefiting from the longer array aperture, higher DOA estimation accuracy can be achieved. Moreover, unlike the conventional DOA estimators, CS estimator won't produce strong grating lobes in the sparse array sampling scenario. Simulation results are presented to validate the effectiveness of the proposed CS-based DOA estimation.
机译:压缩感测(CS)是一项新开发的技术,只要信号在特定域中是稀疏或可压缩的,它就可以仅使用几个随机样本来准确地恢复信号。在本文中,我们旨在利用CS技术来提高雷达的目标到达方向(DOA)估计性能。这个想法是将有限数量的阵列元素部署在更长的孔径中(其中元素间的间距不均匀且远大于一半波长),并使用CS技术估算目标的DOA。受益于更长的阵列孔径,可以实现更高的DOA估计精度。而且,与传统的DOA估计器不同,CS估计器在稀疏阵列采样情况下不会产生强的光栅波瓣。仿真结果被提出来验证所提出的基于CS的DOA估计的有效性。

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