Abstract
In this paper, we propose sense-through-foliage targetdetection 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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Acknowledgments
This work was supported in part by NSFC under Grant 61731006, 61771342, 61711530132, Royal Society of Edinburgh, and Tianjin Higher Education Creative Team Funds Program.
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Zhao, G., Liang, Q., Durrani, T.S. (2020). Hidden Markov Model-Based Sense-Through-Foliage Target Detection Approach. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_84
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DOI: https://doi.org/10.1007/978-981-13-6504-1_84
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