Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 246-255 |
| Number of pages | 10 |
| Journal | International Journal of Wireless Information Networks |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2013 Dec |
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Keywords
- Array antenna
- Fingerprinting
- Hidden Markov model (HMM)
- Viterbi algorithm
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Hardware and Architecture
Cite this
Hidden markov model based localization using array antenna. / Inatomi, Yusuke; Hong, Jihoon; Ohtsuki, Tomoaki.
In: International Journal of Wireless Information Networks, Vol. 20, No. 4, 12.2013, p. 246-255.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hidden markov model based localization using array antenna
AU - Inatomi, Yusuke
AU - Hong, Jihoon
AU - Ohtsuki, Tomoaki
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
UR - http://www.scopus.com/inward/record.url?scp=84890570838&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890570838&partnerID=8YFLogxK
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 -