In this paper, we propose sense-through-foliage target detection approach based on Hidden Markov Models (HMMs). Separate Hidden Markov Models are trained for signals containing target signature and no target (clutter), respectively. Less correlated features are selected as input of Hidden Markov Models for training and testing. Foliage data is collected from three different UWB radar locations, and experimental results show that position 1 data gives the best detection result. All three locations have above 0.8 AUC from the ROC curves.
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