Paper Info
Title
Joint estimation and localization in sensor networks
Abstract
This paper addresses the problem of collaborative estimation and tracking of dynamic phenomena via a wireless sensor network. A distributed linear estimator (i.e., a type of a distributed Kalman filter) is derived. We prove that the filter is mean square consistent when estimating static phenomena. In large sensor networks, it is common that the sensors do not have good knowledge of their locations, which affects the estimation procedure. Unlike existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. A distributed Jacobi algorithm is used to localize the sensors from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and vehicle tracking tasks.
Year
DOI
Venue
2014
10.1109/CDC.2014.7040469
Decision and Control
Keywords
Field
DocType
Kalman filters,convergence,target tracking,wireless sensor networks,auxiliary localization procedure,collaborative estimation,convergence,distributed Jacobi algorithm,distributed Kalman filter,distributed linear estimator,dynamic phenomena,environmental monitoring,joint localization,noisy relative measurements,static phenomena,target estimation approach,target tracking,vehicle tracking tasks,wireless sensor network
Convergence (routing),Mean square,Control theory,Computer science,Jacobi eigenvalue algorithm,Multi-agent system,Kalman filter,Vehicle tracking system,Wireless sensor network,Estimator
Conference
Volume
ISSN
Citations 
abs/1404.3580
0743-1546
4
PageRank 
References 
Authors
0.40
17
4
Name
Order
Citations
PageRank
Nikolay Atanasov116224.84
Roberto Tron240.40
Victor M. Preciado320529.44
George Pappas46632540.42