Through-the-wall radar imaging (TWRI) has attracted a great deal of attention in several sensitive applications, including rescue missions and military operations. Notwithstanding its broad range of applications, TWRI suffers from path-loss because distant targets experience more attenuation of signal power than those closer to the transceiver. This challenge may lead to missed targets with important information necessary for analysis and informed decision making. Responding to the challenge, we have developed a signal model with an effective path-loss compensator incorporating a free space exponent. Furthermore, multipath exploitation and compressive sensing techniques were employed to develop an effective algorithm for isolating residual clutter that may corrupt real targets. The proposed signal model integrates contributions from the front wall, multipath returns, and path-loss. Compared with the state-of-the-art model under the same experimental conditions, simulation results show that the proposed model achieves improved signal-to-clutter ratio, relative clutter peak, and probability of detection by 13.1%, 17.4% and 33.6%, respectively, suggesting that our model can represent the scene more accurately.
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.
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