DocumentCode
2837164
Title
Target tracking algorithm using Gaussian cost-reference particle filter in WSN based on multi-modality information
Author
Zhang, Jian ; Wu, Chengdong ; Jia, Zixi ; Wang, Tianbao
Author_Institution
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
fYear
2010
fDate
26-28 May 2010
Firstpage
1388
Lastpage
1392
Abstract
Target tracking algorithm using Gaussian cost-reference particle filter in WSN based on multi-modality information is proposed in this paper. Compared with WSN relying on sensors of single modality, two different nodes organize the network; Contrary to traditional particle filter algorithm for target tracking, this algorithm does not assume explicit mathematical models of the noise probabilistic distributions, but approximate posterior probability distribution of the state using Gaussian distribution. The mean and variance of Gaussian distribution as information interacted between nodes only need to be transmitted. Simulation results show that the algorithm can satisfy the need of a tracking accuracy and efficiently prolong the network lifetime.
Keywords
Gaussian distribution; Gaussian processes; particle filtering (numerical methods); target tracking; wireless sensor networks; Gaussian cost reference particle filter; Gaussian distribution; WSN; approximate posterior probability distribution; multimodality information; noise probabilistic distribution; target tracking algorithm; wireless sensor network; Acoustic sensors; Energy consumption; Gaussian distribution; Infrared sensors; Magnetic sensors; Particle filters; Probability distribution; Sensor phenomena and characterization; Target tracking; Wireless sensor networks; Gaussian Cost-Reference Particle Filter; Multi-Modality; Target Tracking; Wireless Sensor Networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location
Xuzhou
Print_ISBN
978-1-4244-5181-4
Electronic_ISBN
978-1-4244-5182-1
Type
conf
DOI
10.1109/CCDC.2010.5498195
Filename
5498195
Link To Document