Performance Evaluation of Aspect Dependent-Based Ghost Suppression Methods for Through-the-Wall Radar Imaging

Abstract

There are many approaches which address multipath ghost challenges in Through-the-Wall Radar Imaging (TWRI) under Compressive Sensing (CS) framework. One of the approaches, which exploits ghosts’ locations in the images, termed as Aspect Dependent (AD), does not require prior knowledge of the reflecting geometry making it superior over multipath exploitation based approaches. However, which method is superior within the AD based category is still unknown. Therefore, their performance comparison becomes inevitable, and hence this paper presents their performance evaluation in view of target reconstruction. At first, the methods were grouped based on how the subarrays were applied: multiple subarray, hybrid subarray and sparse array. The methods were fairly evaluated on varying noise level, data volume and the number of targets in the scene. Simulation results show that, when applied in a noisy environment, the hybrid subarray-based approaches were robust than the multiple subarray and sparse array. At 15 dB signal-to-noise ratio, the hybrid subarray exhibited signal to clutter ratio of 3.9 dB and 4.5 dB above the multiple subarray and sparse array, respectively. When high data volumes or in the case of multiple targets, multiple subarrays with duo subarrays became the best candidates.

Keywords: Aspect dependent; compressive sensing; point target; through-wall-radar imaging;