Title
Particle filtering algorithms for tracking a maneuvering target using a network of wireless dynamic sensors
Abstract
We investigate the problem of tracking a maneuvering target using a wireless sensor network. We assume that the sensors are binary (they transmit '1' for target detection and '0' for target absence) and capable of motion, in order to enable the tracking of targets that move over large regions. The sensor velocity is governed by the tracker, but subject to random perturbations that make the actual sensor locations uncertain. The binary local decisions are transmitted over the network to a fusion center that recursively integrates them in order to sequentially produce estimates of the target position, its velocity, and the sensor locations. We investigate the application of particle filtering techniques (namely, sequential importance sampling, auxiliary particle filtering and cost-reference particle filtering) in order to efficiently perform data fusion, and propose new sampling schemes tailored to the problem under study. The validity of the resulting algorithms is illustrated by means of computer simulations.
Year
DOI
Venue
2006
10.1155/ASP/2006/83042
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
particle filter
Computer vision,Importance sampling,Wireless,Computer science,Particle filter,Sensor array,Algorithm,Sensor fusion,Sampling (statistics),Artificial intelligence,Fusion center,Wireless sensor network
Journal
Volume
Issue
ISSN
2006,
1
1687-6180
Citations 
PageRank 
References 
4
0.48
12
Authors
2
Name
Order
Citations
PageRank
Joaquín Míguez125132.71
Antonio Artés-Rodríguez220634.76