Abstract
In this work, an improved approach for throughwall radar imaging is proposed to solve the target detection problem in the presence of clutter with high accuracy and low computational cost. An efficient clutter removal algorithm which combines the Singular Value Decomposition (SVD) of Principal Component Analysis (PCA) and Independent Component Analysis (ICA), (SVDPICA), is investigated to improve the clutter removal performance over the standard PCA and ICA, and to enhance target detection accuracy. An improved approach for through-wall target imaging based on autofocus-backprojection algorithm is proposed to solve the major drawbacks of standard backprojection technique like the phase errors resulted from the uncompensated radar sensor motion, while keeping the computational and memory requirements as low as possible. The capability of the proposed approach (SVDPICA-autofocusbackprojection) is demonstrated by investigating several experimental and simulated through-wall scenarios and comparing the results with other existing techniques. The results reveal the superior performance of the proposed approach in through-wall radar applications.
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