Reconstruction of Sparse Signals in Heterogeneous Radar Sensor Network Based on Distributed Compressive Sensing
首发时间:2015-12-02
Abstract:In this paper, we reconstruct signals in heterogeneous sensor network (HRSN) with distributed compressive sensing (DCS). Combining different types of measurement matrices and different numbers of measurements, we investigate three different scenarios in which HRSN is used to acquiring signals for the first time. In the first scenario, there are two different types of measurement matrices. One is Gaussian measurement and the other is Fourier measurement, and each sensor applies the same numbers of measurements. In the second scenario, all sensors use the same type of measurement matrices but the number of measurements are different each other. The third scenario combines different types of measurement matrix and distinct numbers of measurements. Our simulation results show that in Scenario I, when the common sparsity is considerable, the DCS scheme can reduce the number of measurements. In Scenario II, the reconstruction situation becomes better with the increase of the number of measurements. In both Scenario I and III, joint decoding that use different types of measurement matrices performs better than that of all-Gaussian measurement matrices, but it performs worse than that of all-Fourier measurement matrices. Therefore, DSC is a good compromise between reconstruction percentage and the number of measurements in HRSN.
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基于分布式压缩感知的异构雷达传感网稀疏信号重构
摘要:本文利用分布式压缩感知(DCS)对异构雷达传感网(HRSN)的信号进行重构。针对异构雷达传感网当中的信号获取情况,结合不同类别的测量矩阵和不同的测量矩阵大小,本文首次提出了三种不同情景。在第一种情景当中,系统采用两种不同类别的测量矩阵,其中一种是高斯测量矩阵,另一种是傅立叶测量矩阵。在第二种情景当中,所有的传感器采用相同类别的测量矩阵但是测量矩阵的大小并不相同。第三种情景结合了不同类别的测量矩阵和不同的测量矩阵大小。本文的仿真结果显示,在第一种情景当中,当共同稀疏度较大时,分布式压缩感知能减小测量矩阵的大小。在第二种情景当中,重构情况随着测量矩阵的大小的增大而改善。在第一和第三情景当中,采用不同类别的测量矩阵的联合解码方式性能要优于测量矩阵全是高斯的,但是这种联合解码方式性能又劣于测量矩阵全是傅立叶的。在异构传感网当中,分布式压缩感知是重构性能和测量大小的一个折衷。
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