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Sparse Bayesian Learning for Distributed Passive Radar Imaging via Covariance Sparse Representation

机译:基于协方差稀疏表示的分布式无源雷达成像的稀疏贝叶斯学习

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Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of the distributed passive radar imaging model via Orthogonal Matching Pursuit (OMP) sparse reconstruction is low. For this problem, a framework consisting of sparse representation of the received multi-snapshot radar signal covariance matrix, Sparse Bayesian Learning (SBL) based reconstruction algorithm has been built. At the end of paper, through the simulation experiment, the imaging results of the original data and covariance data at low SNR are compared, and the reconstruction errors under different SNR are used to verify the effectiveness of the proposed algorithm.
机译:由于低成像成本和鲁棒性,使用多个发射器和多个接收器观察目标的分布式无源雷达已成为热门研究。在低SNR的情况下,通过正交匹配追踪(OMP)稀疏重建的分布式无源雷达成像模型的成像精度很低。针对此问题,建立了一个由接收到的多快照雷达信号协方差矩阵的稀疏表示,基于稀疏贝叶斯学习(SBL)的重建算法组成的框架。最后,通过仿真实验,比较了低信噪比下原始数据和协方差数据的成像结果,并利用不同信噪比下的重构误差验证了算法的有效性。

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