UWB Radar Target Detection Based on Hidden Markov Models

In this paper, we propose ultra-wideband (UWB) radar target detection approach based on Hidden Markov Models (HMMs). HMMs are used as a classifier to identify signal with the presence of target in a background clutter and the pure clutter response signal. Time-frequency features are extracted and features have less correlation to each other are selected based on the feature covariance matrix and fed into HMMs. The detection experiments are conducted in two different scenarios: sense-through-foliage target detection and sense-through-wall human detection. The sense-through-foliage data set contains poor quality UWB radar return echoes using low amplitude transmitting pulses. Data collected from different radar locations are tested and detection results are presented. Sense-through-wall data are collected using different UWB radar and the target is human standing behind different types of walls. HMMs parameters are also investigated to optimally model UWB radar signals for target detection.

[1]  Baoju Zhang,et al.  Experimental study of through-wall human detection using ultra wideband radar sensors , 2014 .

[2]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[3]  Pierre A. Devijver,et al.  Baum's forward-backward algorithm revisited , 1985, Pattern Recognit. Lett..

[4]  Louis A. Liporace,et al.  Maximum likelihood estimation for multivariate observations of Markov sources , 1982, IEEE Trans. Inf. Theory.

[5]  Guerino Giancola,et al.  Understanding Ultra Wide Band Radio Fundamentals , 2004 .

[6]  Hermann Ney,et al.  Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Qilian Liang,et al.  Situation Understanding Based on Heterogeneous Sensor Networks and Human-Inspired Favor Weak Fuzzy Logic System , 2009, IEEE Systems Journal.

[8]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[9]  Li Sheng,et al.  Sense through wall human detection using UWB radar , 2011, EURASIP J. Wirel. Commun. Netw..

[10]  Jeffrey H. Reed,et al.  Introduction to ultra wideband communication systems, an , 2005 .

[11]  B.-H. Juang,et al.  Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains , 1985, AT&T Technical Journal.

[12]  Jing Liang,et al.  Foliage Clutter Modeling Using the UWB Radar , 2008, 2008 IEEE International Conference on Communications.

[13]  Francis C. M. Lau,et al.  Sense-through-wall human detection using the UWB radar with sparse SVD , 2014, Phys. Commun..

[14]  Qilian Liang,et al.  KUPS: Knowledge-based Ubiquitous and Persistent Sensor networks for Threat Assessment , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[15]  Qilian Liang.  Biologically-Inspired Target Recognition in Radar Sensor Networks , 2009, WASA.

[16]  Jing Liang,et al.  Outdoor Propagation Channel Modeling in Foliage Environment , 2010, IEEE Transactions on Vehicular Technology.

[17]  Xiuzhen Cheng,et al.  NEW: Network-Enabled Electronic Warfare for Target Recognition , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[20]  Xiuzhen Cheng,et al.  Opportunistic Sensing in Wireless Sensor Networks: Theory and Application , 2014, IEEE Trans. Computers.

[21]  Jing Liang,et al.  Sense-Through-Foliage target detection using UWB radar sensor networks , 2010, Pattern Recognit. Lett..

[22]  Jin-Hyung Kim,et al.  An HMM-Based Threshold Model Approach for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Qilian Liang,et al.  Multistep Information Fusion for Target Detection Using UWB Radar Sensor Network , 2015, IEEE Sensors Journal.

[24]  Hsiao-Wuen Hon,et al.  Speaker-independent phone recognition using hidden Markov models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[25]  Xiuzhen Cheng,et al.  UWB Radar Sensor Networks for Sense-Through-Foliage Target Detection , 2008, 2008 IEEE International Conference on Communications.

[26]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[28]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[29]  Biing-Hwang Juang,et al.  Maximum likelihood estimation for multivariate mixture observations of markov chains , 1986, IEEE Trans. Inf. Theory.

[30]  Jing Liang,et al.  A Propagation Environment Modeling in Foliage , 2010, EURASIP J. Wirel. Commun. Netw..

[31]  R. Bakis.  Continuous speech recognition via centisecond acoustic states , 1976 .

[32]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[33]  Pedro J. Moreno,et al.  Using the Fisher kernel method for Web audio classification , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[34]  Regina Barzilay,et al.  Incorporating Content Structure into Text Analysis Applications , 2010, EMNLP.