Fast sparse image reconstruction models in through-the-wall radars: a review

Aude Kileo, Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, P. O. Box 33335, Dar es Salaam, Tanzania.Follow
Hashimu U. Iddi, Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, P. O. Box 33335, Dar es Salaam, Tanzania.Follow
Abdi Abdalla, Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, P. O. Box 33335, Dar es Salaam, Tanzania.Follow

Section

Physical Sciences

Abstract

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. This paper presents a comprehensive review of CS- based reconstruction algorithms for TWRI, with an emphasis on sparse signal reconstruction. A systematic literature review was conducted, identifying 351 academic sources, of which 90 were selected after abstract screening. These were further refined to 33 papers focused on recent reconstruction algorithms published between 2016 and 2025. Key findings highlight that the Limited Memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm, when paired with the Euclidean norm and regularised least squares, LBFGS demonstrates significant computational efficiency when applied to smooth, regularized least-squares formulations of the reconstruction problem, although it does not directly enforce sparsity constraints. Simulation results from (Mwisomba et al. (2022)) show that LBFGS reduces computation time by 87% compared to traditional methods, even with larger datasets or more complex scenes, while maintaining robustness in noisy environments. The review also outlines recent advancements in sparse optimisation and identifies future research directions to improve CS-TWRI applications, especially for real-time deployment scenarios.

Recommended Citation

Kileo, Aude; Iddi, Hashimu U.; and Abdalla, Abdi (2026) "Fast sparse image reconstruction models in through-the-wall radars: a review," Tanzania Journal of Science: Vol. 52: Iss. 3, Article 19.
Available at:https://doi.org/10.65085/2507-7961.2378