Paper Info
Title
Multi-Model Rao-Blackwellised Particle Filter for Maneuvering Target Tracking in Distributed Acoustic Sensor Networks
Abstract
In this paper, a multi-model Rao-Blackwellised particle filter algorithm is presented for tracking high maneuvering target in distributed acoustic sensor networks. It is more efficient for high-dimension nonlinear and non-Gaussian estimation problems than generic particle filter, and by stratified particles sampling from a set of system models, it can tackle the target's maneuver perfectly. In the simulation comparison, a high maneuvering target moves through an acoustic sensor network field. The target is tracked using both the RBPF and the multi-model RBPF algorithms, and a location-central protocol is applied for energy conservation. The results show that our approach has great performance improvements, especially when the target is making maneuver.
Year
DOI
Venue
2007
10.1109/ICASSP.2007.367061
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference
Keywords
Field
DocType
non-Gaussian estimation problems,distributed acoustic sensor networks,Multi-model,particle filtering (numerical methods),multi-model,RBPF,signal sampling,Maneuvering target,target tracking,matrix algebra,particle filter,maneuvering target,multi-model Rao-Blackwellised particle filter,maneuvering target tracking,sensor networks,acoustic signal processing,stratified particles sampling,distributed sensors,Particle filter,acoustic transducers,rbpf
Energy conservation,Nonlinear system,Computer science,Matrix algebra,Control theory,Particle filter,Acoustic sensor,Sampling (statistics),Particle filtering algorithm,Wireless sensor network
Conference
Volume
Issue
ISSN
3
null
1520-6149
ISBN
Citations 
PageRank 
1-4244-0727-3
1
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Yu Zhi-jun110.36
You Guang-xin210.36
Wei Jian-ming310.36
Haitao Liu415111.39