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Source number estimation algorithm for wideband LFM signal based on compressed sensing

机译:基于压缩感知的宽带LFM信号源数估计算法

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Because of the samples which gained through compressive measurement effectively preserve the features information of the original signal, Compressed Sensing (CS) has been successfully applied to the detection and parameter estimation of Linear Frequency Modulation (LFM) signal. However, the source number of multi-component signal often be used as a prior knowledge which is out of accord with the practice. In this paper, we proposed a source number estimation algorithm based on compressed sensing. In particular, we analyzed the algorithm's shortcoming under low Signal to Noise Ratio (SNR) condition in theory and proposed an improved algorithm to solve the problem. The theoretical analysis and simulation results both proved that the proposed improved algorithm has a better anti-noise performance than the original algorithm under the condition SNR ≤ 3dB. Furthermore, compared with the conventional algorithm, the improved algorithm in this paper could obtain a higher correct rate of source number estimation with few samples under low SNR.
机译:由于通过压缩测量获得的样本有效地保留了原始信号的特征信息,因此压缩感知(CS)已成功地应用于线性调频(LFM)信号的检测和参数估计。然而,多分量信号的源编号经常被用作与实践不符的先验知识。本文提出了一种基于压缩感知的信源数估计算法。特别是,我们从理论上分析了低信噪比条件下算法的缺点,提出了一种改进的算法来解决该问题。理论分析和仿真结果均证明,该算法在信噪比≤3dB的情况下具有比原算法更好的抗噪性能。此外,与常规算法相比,本文提出的改进算法在低信噪比的情况下,样本数较少,可以获得较高的正确源数估计正确率。

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