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Robust Through-the-Wall Radar Image Classification Using a Target-Model Alignment Procedure
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AI
DOI：10.1109/TIP.2011.2166967.png)
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Abstract
En 中文
A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) <= 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates >= 97%.
Keywords:
Image classification
radar
radar imaging
synthetic aperture radar
target recognition
through-the-wall radar
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W