TY - GEN
T1 - Emitter localization using a single moving observer based on UKF
AU - Yilong, Zhu
AU - Shuguo, Xie
AU - Meiling, Yang
AU - Ming, Zuo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper, a method based on UKF (The Unscented Kalman Filter) for emitter localization using a single moving observer is proposed. This method considers an emitter localization problem using a number of received signal strength (RSS) data. The state equation and observation equation, which contain variables of emitter location, are firstly established. Wavelet analysis algorithm is proposed for RSS data smoothing, eliminating the noises brought by multipath effect at some extent. With these smoothed RSS data, the state vector continuously estimated and corrected with the iteration of UKF. The unknown variables of emitter location converge gradually and fluctuate at a certain value finally. The Kmeans clustering algorithm is used to cluster these convergent estimations, then the optimal estimation of the emitter location is obtained. Finally, an experiment is designed to verify the feasibility of this method. We built a test system with Universal Software Radio Peripheral (USRP) and GPS, measuring RSS data and GPS data respectively. The experimental results show that the estimated location of the emitter is closed to its actual location with a small localization error, verifying the validity of this localization method.
AB - In this paper, a method based on UKF (The Unscented Kalman Filter) for emitter localization using a single moving observer is proposed. This method considers an emitter localization problem using a number of received signal strength (RSS) data. The state equation and observation equation, which contain variables of emitter location, are firstly established. Wavelet analysis algorithm is proposed for RSS data smoothing, eliminating the noises brought by multipath effect at some extent. With these smoothed RSS data, the state vector continuously estimated and corrected with the iteration of UKF. The unknown variables of emitter location converge gradually and fluctuate at a certain value finally. The Kmeans clustering algorithm is used to cluster these convergent estimations, then the optimal estimation of the emitter location is obtained. Finally, an experiment is designed to verify the feasibility of this method. We built a test system with Universal Software Radio Peripheral (USRP) and GPS, measuring RSS data and GPS data respectively. The experimental results show that the estimated location of the emitter is closed to its actual location with a small localization error, verifying the validity of this localization method.
KW - Emitter localization
KW - Kmeans clustering
KW - RSS
KW - UKF
KW - Wavelet analysis
UR - https://www.scopus.com/pages/publications/85047727894
U2 - 10.1109/ICCT.2017.8359816
DO - 10.1109/ICCT.2017.8359816
M3 - 会议稿件
AN - SCOPUS:85047727894
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 1157
EP - 1161
BT - 2017 17th IEEE International Conference on Communication Technology, ICCT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Communication Technology, ICCT 2017
Y2 - 27 October 2017 through 30 October 2017
ER -