DocumentCode :
149561
Title :
Distributed joint estimation and identification for sensor networks with unknown inputs
Author :
Hua Lan ; Bishop, Adrian N. ; Quan Pan
Author_Institution :
Sch. of Autom., Northwestern Polytech. Univ., Xi´an, China
fYear :
2014
fDate :
21-24 April 2014
Firstpage :
1
Lastpage :
6
Abstract :
In this paper we consider the problem of distributed, joint, state estimation and identification for a class of stochastic systems with unknown inputs (UI). A distributed Expectation-Maximization (EM) algorithm is presented to estimate the local state at each sensor node by using the local observations in the E-step, and three different consensus schemes are proposed to diffuse the local state estimate to each sensor´s neighbours and to derive the global state estimate at each node. In the M-step, each sensor identifies the local unknown inputs by using the global state estimate. A numerical example of target tracking in distributed sensor network is given to verify the three different distributed EM algorithms compared with the centralized EM based measurement-level and track-level fusion.
Keywords :
expectation-maximisation algorithm; sensor fusion; state estimation; target tracking; wireless sensor networks; E-step; EM algorithm; M-step; centralized EM based measurement-level; distributed expectation-maximization algorithm; distributed joint estimation-identification; distributed sensor network; global state estimate; state estimation; stochastic systems; target tracking; track-level fusion; Computer architecture; Educational institutions; Joints; Kalman filters; State estimation; Stochastic systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4799-2842-2
Type :
conf
DOI :
10.1109/ISSNIP.2014.6827600
Filename :
6827600
Link To Document :
بازگشت