TY - JOUR
T1 - Hidden markov model based localization using array antenna
AU - Inatomi, Yusuke
AU - Hong, Jihoon
AU - Ohtsuki, Tomoaki
N1 - Funding Information:
B.E., M.E., and Ph.D. Degrees in Electrical Engineering from Keio University, Yokohama, Japan in 1990, 1992, and 1994, respectively. From 1994 to 1995 he was a Post-doctoral Fellow and a Visiting Researcher in Electrical Engineering at Keio University. From 1993 to 1995 he was a Special Researcher of Fellowships of the Japan Soci- ety for the Promotion of Science for Japanese Junior Scientists. From 1995 to 2005 he was with Science University of Tokyo. In 2005 he joined Keio University. He is now a Professor at Keio University. From 1998 to 1999 he was with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. He is engaged in research on wireless communications, optical communications, signal processing, and information theory. Dr. Ohtsuki is a recipient of the 1997 Inoue Research Award for Young Scientist, the 1997 Hiroshi Ando Memorial Young Engineering Award, Ericsson Young Scientist Award 2000, 2002 Funai Information and Science Award for Young Scientist, IEEE the 1st Asia-Pacific Young Researcher Award 2001, the 5th International Communication Foundation (ICF) Research Award, 2011 IEEE SPCE Outstanding Service Award, the 28th TELECOM System Technology Award, and ETRI Journal’s 2012 Best Reviewer Award. He has published more than 115 journal papers and 270 international conference papers. He served a Chair of IEEE Communications Society, Signal Processing for Communications and Electronics Technical Committee. He served a Technical Editor of the IEEE Wireless Communications Magazine. He is now serving an Editor of the IEEE Communications Surveys and Tutorials, IEICE Communications Express, and Elsevier Physical Communications. He has served symposium Co-chair of many conferences, including IEEE GLOBECOM 2008, SPC, IEEE ICC2011, CTS, and IEEE GCOM2012, SPC. He gave tutorials and keynote speech at many international conferences including IEEE VTC, IEEE PIMRC, and so on. He is a senior member of the IEEE and the IEICE.
PY - 2013/12
Y1 - 2013/12
N2 - We present a hidden Markov model (HMM) based localization using array antenna. In this method, we use the eigenvector spanning signal subspace as a location dependent feature. The eigenvector does not depend on received signal strength but on direction of arrival of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the radio wave. The conventional localization method based on fingerprinting does not take previous information into account. In our proposal algorithm with HMM, we take previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference point according to setting in advance. In an indoor environment represented in a quantized grid, we design the transition probability due to previous estimated position. Because of this, target's movable range is obtained. In addition, we use maximum likelihood estimation method based on statics of correlation values. The correlation value is an indicator of pattern matching in a fingerprinting method. The most likely trajectory is calculated by Viterbi algorithm with above mentioned probabilities. The experimental results show that the localization accuracy is improved owing to the use of HMM.
AB - We present a hidden Markov model (HMM) based localization using array antenna. In this method, we use the eigenvector spanning signal subspace as a location dependent feature. The eigenvector does not depend on received signal strength but on direction of arrival of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the radio wave. The conventional localization method based on fingerprinting does not take previous information into account. In our proposal algorithm with HMM, we take previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference point according to setting in advance. In an indoor environment represented in a quantized grid, we design the transition probability due to previous estimated position. Because of this, target's movable range is obtained. In addition, we use maximum likelihood estimation method based on statics of correlation values. The correlation value is an indicator of pattern matching in a fingerprinting method. The most likely trajectory is calculated by Viterbi algorithm with above mentioned probabilities. The experimental results show that the localization accuracy is improved owing to the use of HMM.
KW - Array antenna
KW - Fingerprinting
KW - Hidden Markov model (HMM)
KW - Viterbi algorithm
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U2 - 10.1007/s10776-013-0211-y
DO - 10.1007/s10776-013-0211-y
M3 - Article
AN - SCOPUS:84890570838
VL - 20
SP - 246
EP - 255
JO - International Journal of Wireless Information Networks
JF - International Journal of Wireless Information Networks
SN - 1068-9605
IS - 4
ER -