Relaying Kalman filters for sensor networks

Author

Liu, Zhigang ; Wang, Jinkuan ; Qu, Wei

Author_Institution

Insitute of Eng. Optimization & Smart Antenna, Northeastern Univ., Qinhuangdao, China

Volume

2

fYear

2010

fDate

25-27 June 2010

Abstract

Due to limited sensing range for sensors, moving object tracking has to be realized by relaying from one sensor to the other in sensor networks, and so the tracking procedure can be modelled as a Markov chain system. Based on the Bayesian theory, we propose the relaying Kalman filter(RKF) algorithm which introduce the equations of updating sensor probability, and reconstruct the innovation equation. Furthermore, the extension of the proposed method can be applied in nonlinear dynamic system. Finally, simulation results show the effectiveness of the proposed RKF algorithm.

Keywords

Bayes methods; Kalman filters; Markov processes; ad hoc networks; wireless sensor networks; Bayesian theory; Markov chain system; relaying Kalman filter algorithm; sensor networks; Bayesian methods; Collaboration; Filtering theory; Intelligent sensors; Kalman filters; Nonlinear equations; Relays; Sensor systems; Technological innovation; Time measurement; Bayesian theory; Kalman filter; Markov chain; target tracking; wireless sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Design and Applications (ICCDA), 2010 International Conference on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-7164-5

Electronic_ISBN

978-1-4244-7164-5

Type

conf

DOI

10.1109/ICCDA.2010.5541281

Filename

5541281