DocumentCode :
2114676
Title :
Weighted Average Approach to Quantized Measurement Fusion in Wireless Sensor Network
Author :
Zhou, Yan ; Li, Jianxun
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2009
fDate :
24-26 Sept. 2009
Firstpage :
1
Lastpage :
5
Abstract :
The quantized measurement fusion problem for target tracking in a sensor network is investigated using a weighted average approach. The measurement in each local sensor is quantized by uniform quantization and then transmitted to a fusion center (FC). To estimate the state of the target in the FC, the quantized messages are first to be combined in a weighted average way instead of merging all the quantized messages to a vector. Then extended Kalman filtering (EKF) is employed to estimate the target state. Focuses are on tradeoff between bandwidth of each sensor and the global tracking accuracy. The closed-form solution to the optimization problem for bandwidth scheduling is given, where the mean square error (MSE) incurred by weighted average fusion is minimized subject to a constraint on the total energy consumption. Nonlinear Gaussian discrete-time system model following the EKF principle is employed. Simulation example is given to illustrate the proposed scheme can obtain average percentage of energy saving up to 37.2% with computational burden reduction 32%.
Keywords :
Gaussian processes; Kalman filters; mean square error methods; nonlinear filters; wireless sensor networks; extended Kalman filtering; fusion center; mean square error; nonlinear Gaussian discrete-time system model; quantized measurement fusion problem; weighted average approach; wireless sensor network; Bandwidth; Closed-form solution; Filtering; Kalman filters; Merging; Quantization; Sensor fusion; State estimation; Target tracking; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2009. WiCom '09. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-3692-7
Electronic_ISBN :
978-1-4244-3693-4
Type :
conf
DOI :
10.1109/WICOM.2009.5302588
Filename :
5302588
Link To Document :
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