Abstract:

Unlike the traditional omnidirectional sensors,directional sensors always play their roles within a special range.Aiming at the target tracking with distance- dependent measurement noises in directional sensor networks (DSNs),the distance- dependent measurement error of sensors is modeled as a multiplicative noise,and a distribu-ted target tracking algorithm is proposed based on Bayesian estimation.This algorithm restrains the target estimation within a restricted rectangle area by using the range information of the target detected by directional sensors.Simu-lation results show that,as compared with the conventional extended Kalman filtering and the estimator combining the maximum likelihood with the Kalman filtering,the proposed algorithm can accurately and effectively track mo-ving targets in complex measurement environments.

Key words: target tracking, directional sensor network, Kalman filtering, distance measurement