Source localization with sparse recovery for coherent far- and near-field signals

A. Elbir, T. E. Tuncer
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引用次数: 3

Abstract

In source localization applications, coherency among the signals is an important source of error for parameter estimation. In this paper, a method is proposed to solve the localization problem where there are coherently mixed arbitrary number of far- and near-field sources. In order to estimate the direction-of-arrival (DOA) and the range parameters, compressed sensing (CS) approach is presented where a dictionary matrix is constructed with far- and near-field steering vectors. A sparse vector including the supports of the source signals is estimated in spatial domain. The supports of coherent signals are recovered by using convex minimization techniques. It is shown that the proposed approach recovers the signal components of the array output as well as determining the source locations.
相干远场和近场信号稀疏恢复的源定位
在源定位应用中,信号间的相干性是参数估计误差的重要来源。本文提出了一种解决任意数量的远场和近场相干混合源的定位问题的方法。为了估计到达方向(DOA)和距离参数,提出了一种压缩感知(CS)方法,该方法由远场和近场转向矢量构造字典矩阵。在空间域中估计一个包含源信号支持度的稀疏向量。利用凸极小化技术恢复相干信号的支撑点。结果表明,该方法不仅可以恢复阵列输出的信号分量,而且可以确定源的位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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