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

Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method

Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing. In this paper, we deal with obtaining sparse solution to near- field radar imaging problems by developing efficient sparse reconstruction, which avoid storing and using large-scale sensing matrices. We demonstrate that the "fast multipole method" can be employed within sparse reconstruction algorithms to efficiently compute the sensing operator and its adjoint (backward) operator, hence improving the computation speed and memory usage, especially for large-scale 3-D imaging problems. For several near-field imaging scenarios including point scatterers and 2-D/3-D extended targets, the performances of sparse reconstruction algorithms are numerically tested in comparison with a classical solver. Furthermore, effectiveness of the fast multipole method and efficient reconstruction are illustrated in terms of memory requirement and processing time

Smart bricks for strain sensing and crack detection in masonry structures

The paper proposes the novel concept of smart bricks as a durable sensing solution for structural health monitoring of masonry structures. The term smart bricks denotes piezoresistive clay bricks with suitable electronics capable of outputting measurable changes in their electrical properties under changes in their state of strain. This feature can be exploited to evaluate stress at critical locations inside a masonry wall and to detect changes in loading paths associated with structural damage, for instance following an earthquake. Results from an experimental campaign show that normal clay bricks, fabricated in the laboratory with embedded electrodes made of a special steel for resisting the high baking temperature, exhibit a quite linear and repeatable piezoresistive behavior. That is a change in electrical resistance proportional to a change in axial strain. In order to be able to exploit this feature for strain sensing, high-resolution electronics are used with a biphasic DC measurement approach to eliminate any resistance drift due to material polarization. Then, an enhanced nanocomposite smart brick is proposed, where titania is mixed with clay before baking, in order to enhance the brick\u27s mechanical properties, improve its noise rejection, and increase its electrical conductivity. Titania was selected among other possible conductive nanofillers due to its resistance to high temperatures and its ability to improve the durability of construction materials while maintaining the aesthetic appearance of clay bricks. An application of smart bricks for crack detection in masonry walls is demonstrated by laboratory testing of a small-scale wall specimen under different loading conditions and controlled damage. Overall, it is demonstrated that a few strategically placed smart bricks enable monitoring of the state of strain within the wall and provide information that is capable of crack detection

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

undefine