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首页> 外文会议>2013 3rd International Conference on Consumer Electronics, Communications and Networks >Robustly blind sparsity signal recovery algorithm for compressive sensing radar
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Robustly blind sparsity signal recovery algorithm for compressive sensing radar

机译：压感雷达的鲁棒稀疏稀疏信号恢复算法

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摘要

Compressive sensing (CS) is an emerging data acquisition method under the condition that the signal is sparse or compressible. However, applying CS in radar to reconstruct target scene always requires the sparsity of the echo signal is known priori with high Signal to Interference and Noise Ratio (SINR). Such an ideal assumption may not be met in practical situations. In this paper, a robustly blind sparsity recovery algorithm for compressive sensing radar (CSR) is presented. The proposed method could enhance the performance of targets detection and range-Doppler parameters estimation in low SINR without known the sparsity of the original signal with the idea of choosing supplements of the sparse signal adaptively and optimizing transmit waveform. The numerical simulations are carried out to verify the effectiveness of the proposed method.
机译：在信号稀疏或可压缩的情况下，压缩感测（CS）是一种新兴的数据采集方法。然而，将CS应用在雷达中以重建目标场景总是需要回声信号的稀疏性，这是先验已知的，具有很高的信干噪比（SINR）。在实际情况下可能无法满足这样的理想假设。本文提出了一种用于压缩感知雷达（CSR）的鲁棒稀疏稀疏恢复算法。提出的方法可以在低SINR的情况下提高目标检测和距离多普勒参数估计的性能，而无需知道原始信号的稀疏性，从而可以自适应地选择稀疏信号的补充并优化发射波形。通过数值模拟验证了所提方法的有效性。

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