A Novel Hybrid Autofocusing Technique for Compressive Sensing-Based Through-the-Wall Radar Imaging With Synthetic Aperture Radar System

Le résumé fourni par la source

Autofocusing is an approach for real-time through the wall imaging under unknown wall characteristics. Conventional autofocusing methods operate on synthetic aperture radar which require fully and uniformly sampled data, large bandwidths, and wide apertures leading to longer acquisition times, high memory needs, and increased computational burden. Reducing the sampling rate to ease these demands can cause information loss or artifacts, while the iterative nature of autofocusing further increases complexity. Moreover, the conventional autofocusing methods are computationally expensive and time-consuming because it involves a two dimensional (2D) search over wall parameters typically wall thickness and relative permittivity. Reconstructing and storing multiple radar images during the search process can consume significant memory. Motivated by these challenges, there exist a need for developing an efficient autofocusing technique for synthetic aperture radar based through the wall radar imaging systems. This paper proposes a hybrid autofocusing method that integrates compressive sensing with through the wall radar image using synthetic aperture radar. The proposed method estimates the wall's permittivity directly from compressed data and adjusts only wall thickness during autofocusing, reducing both data and computational load. Thus, the hybrid autofocusing method will transform two-dimensional searching procedure into a one-dimensional problem and integrating it with compressed sensing will ensure fast and accurate through the wall radar image with reduced data samples, making it suitable for real-time applications. The proposed autofocusing approach was tested using real data from a stepped-frequency through the wall radar imaging system (3.5 GHz–5.5 GHz). The well-focused image obtained in the experiment validated the effectiveness of this method.

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