Compressed sensing for enhanced through-the-wall radar imaging

Through-the-wall radar imaging (TWRI) is an emerging technology that aims to capture scenes behind walls and other visually opaque materials. The abilities to sense through walls are highly desirable for both military and civil applications, such as search and rescue missions, surveillance, and reconnaissance. TWRI systems, however, face with several challenges including prolonged data acquisition, large objects, strong wall clutter, and shadowing effects, which limit the radar imaging performances and hinder target detection and localization

A Rank-Deficient and Sparse Penalized Optimization Model for Compressive Indoor Radar Target Localization

This paper proposes a rank-deficient and sparse penalized optimization method for addressing the problem of through-wall radar imaging (TWRI) in the presence of structured wall clutter. Compressive TWRI enables fast data collection and accurate target localization, but faces with the challenges of incomplete data measurements and strong wall clutter. This paper handles these challenges by formulating the task of wall-clutter removal and target image reconstruction as a joint low-rank and sparse regularized minimization problem. In this problem,  the low-rank regularization is used to capture the low-dimensional structure of the wall signals and the sparse penalty is employed to represent the image of the indoor targets. We introduce an iterative algorithm based on the forward-backward proximal gradient technique to solve the large-scale optimization problem, which simultaneously removes unwanted wall clutter and reconstruct an image of indoor targets. Simulated and real radar data are used to validate the effectiveness of the proposed rank-deficient and sparse regularized optimization approach

Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review

Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes.AKey words: spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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