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Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

Published online by Cambridge University Press:  16 June 2021

Candida Mwisomba ,
Abdi T. Abdalla Open the ORCID record for Abdi T. Abdalla [Opens in a new window] ,
Idrissa Amour ,
Florian Mkemwa  and
Baraka Maiseli
Candida Mwisomba
Affiliation:
Department of Electronics and Telecommunications Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
Abdi T. Abdalla*
Affiliation:
Department of Electronics and Telecommunications Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
Idrissa Amour
Affiliation:
Department of Mathematics, University of Dar es Salaam, Dar es Salaam, Tanzania
Florian Mkemwa
Affiliation:
Department of Electronics and Telecommunications Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
Baraka Maiseli
Affiliation:
Department of Electronics and Telecommunications Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
*
Author for correspondence: Abdi T. Abdalla, E-mail: [email protected]
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Abstract

Compressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

Keywords

Compressive sensingfast sparse image reconstructionLBFGS algorithmthrough-the-wall radar imaging
Type
Radar
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Special Issue 6: EuCAP 2020 Special Issue , July 2022 , pp. 739 - 749
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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