Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging

Lele Qu,Shimiao An,Yanpeng Sun

doi:10.1155/2019/5651602

Lele Qu, Shimiao An + Show 1 more

Open Access

https://doi.org/10.1155/2019/5651602

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Abstract

Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.

Highlights

Through-the-wall radar imaging (TWRI) is a promising technique to detect, localize, and identify the objects behind the walls [1,2,3]
Due to the shadowing effect and the limited scattering range of targets behind walls, the echoes of targets measured from single view have relatively weak energy, which may lead to inferior imaging quality
A distributed greedy signal recovery algorithm called censored simultaneous orthogonal matching pursuit (CSOMP) [20] without the centralized data accumulation for multiview TWRI is proposed, which has the advantage of reducing the communication costs among all radar units

Summary

Introduction

Through-the-wall radar imaging (TWRI) is a promising technique to detect, localize, and identify the objects behind the walls [1,2,3]. A multiview TWRI formation algorithm based on joint Bayesian sparse recovery framework is proposed to enhance imaging quality in the strong wall clutter environment [17]. A distributed greedy signal recovery algorithm called censored simultaneous orthogonal matching pursuit (CSOMP) [20] without the centralized data accumulation for multiview TWRI is proposed, which has the advantage of reducing the communication costs among all radar units. International Journal of Antennas and Propagation imaging approach requires prior information such as the sparsity or noise level for accurate imaging reconstruction Such information is generally not available in practical multiview TWRI scenarios. The proposed imaging approach can provide the accurate imaging reconstruction results without prior knowledge such as sparsity or noise level while reducing the communication costs among radar units from various views.

Signal Model

Proposed Algorithm Description

Results and Discussion

Conclusion