DocumentCode :
2114454
Title :
EKF-Based Adaptive Sensor Scheduling for Target Tracking
Author :
Liu, Yang ; Sun, Zhendong
Author_Institution :
Center for Control & Optimization, South China Univ. of Technol., Guangzhou
Volume :
2
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
171
Lastpage :
174
Abstract :
For Wireless Sensor Networks (WSN), target tracking is a canonical problem that collaborates signal and information processing to dynamically manage sensor resources and efficiently process distributed sensor measurements. This paper proposes an adaptive sensor scheduling strategy that jointly sets up distribute dynamic clustering, selects the tasking sensor, and determines the sampling interval. The approach utilizes Least-Square (LSQ) in initializing, Extended Kalman Filter (EKF) in tracking accuracy estimation, and adaptive sampling in velocity prediction. Simulation results demonstrate significant improvement in tracking accuracy compared to the non-adaptive approaches.
Keywords :
Kalman filters; target tracking; wireless sensor networks; adaptive sampling; adaptive sensor scheduling; distributed sensor measurement; dynamic clustering; extended Kalman filter; information processing; sensor resources; target tracking; tasking sensor; tracking accuracy estimation; velocity prediction; wireless sensor network; EKF; WSN; adaptive sensor scheduling; target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering, 2008. ISISE '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-2727-4
Type :
conf
DOI :
10.1109/ISISE.2008.286
Filename :
4732368
Link To Document :
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