Passive Source Localization Using Compressive Sensing
Passive Source Localization Using Compressive Sensing
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DOI：
10.3390/s19204522
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Hangfang Zhao;M. Irshad;Huihong Shi-;Wen Xu
中科院分区：
文献类型：
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作者：
Hangfang Zhao;M. Irshad;Huihong Shi-;Wen Xu
本文提出了一种利用欠定线性反演问题进行水下被动源定位的方法。通过利用源信号的空间稀疏性，使用稀疏重建算法来评估指定网格上的信号强度。我们的策略导致高比率的测量稀疏性（RMS），在低旁瓣水平的峰值锐度的增加，和最小化的问题的维数由于制定的系统方程的多个快照的数据相关矩阵的基础上。此外，为了减少计算负担，提出了Bartlett预定位。我们所提出的技术可以执行接近Bartlet和白色噪声增益约束过程中的单源的情况下，但它可以给出稍好的结果，同时定位多个源。该方法具有Bartlett和白色噪声增益约束方法各自的特点，如对环境/系统失配的鲁棒性和高分辨率。仿真和实验数据的处理证明了该方法对水下声源定位的有效性。
This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.