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Through-the-wall radar (TWR) images of stationary target behind a wall are subject to strong stationary and non-stationary clutter which obscures the target position and size in the image. Stationary clutters are present due to strong reflection from the wall and non-stationary clutter occurs due to multipath, noise, etc. A lot of work has been reported for mitigating stationary clutter successfully for various real scenarios. However, for mitigating nonstationary clutter various authors have reported their work in this field but any concrete result has not been reported so far. Hence, there is a need for an optimal methodology to mitigate non-stationary clutter in TWR images for achieving a high-quality image representing the target position, shape and its size. Till now it is difficult to achieve shape detection of the target from the TWR system. Therefore, in this paper, a novel optimal thresholding technique is proposed to mitigating non-stationary clutter for further enhancement on shape detection of a target using curve fitting and genetic algorithm. The proposed methodology gives a satisfactory result and can prove to be a powerful technique for minimising clutter.

Development of Optimal Thresholding Technique for Shape and Size Detection for Through the wall Radar Imaging System