Paper Info
Title
Cooperative localization using efficient Kalman filtering for mobile wireless sensor networks
Abstract
We consider the problem of cooperative localization in mobile wireless sensor networks (WSNs). To be able to continuously localize the mobile network, we propose to exploit the knowledge of the location of the anchor nodes to linearize the nonlinear distance measurements with respect to the location of the unknown nodes. Based on this linearized measurement model, we estimate the location of the unknown nodes using a Kalman filter (KF) instead of a suboptimal extended KF (EKF) and try to estimate the corresponding unknown measurement noise covariance matrix using an iterative process. The simulation results illustrate that the proposed algorithm (only with a few iterations) attains the posterior Cramer-Rao bound (PCRB) of mobile location estimation and clearly outperforms related anchorless and anchored mobile localization algorithms.
Year
Venue
Keywords
2011
Barcelona
kalman filters,cooperative communication,covariance matrices,distance measurement,estimation theory,mobile radio,nonlinear filters,radio direction-finding,radiotelemetry,wireless sensor networks,ekf,pcrb,wsn,anchor node localization,cooperative localization,iterative process,linearized measurement model,mobile location estimation,mobile wireless sensor network,noise covariance matrix,nonlinear distance measurement,posterior cramer-rao bound,suboptimal extended kalman filtering,mobile communication,mathematical model,noise,estimation,mobile computing
Field
DocType
ISSN
Mobile computing,Extended Kalman filter,Iterative and incremental development,Control theory,Computer science,Algorithm,Kalman filter,Cellular network,Covariance matrix,Wireless sensor network,Mobile telephony
Conference
2076-1465
Citations 
PageRank 
References 
2
0.41
5
Authors
3
Name
Order
Citations
PageRank
Hadi Jamali Rad11029.18
Toon van Waterschoot215714.29
G. Leus34344307.24