TY - GEN

T1 - Incremental recursive prediction error algorithm for parameter estimation in sensor networks

AU - Sundhar Ram, S.

AU - Veeravalli, V. V.

AU - Nedić, A.

PY - 2008/12/1

Y1 - 2008/12/1

N2 - We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect, we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms. We study the convergence behavior of the algorithm and provide sufficient conditions for its convergence. Our convergence result is rather general and contains as special cases the known convergence results for the incremental versions of the least-mean square algorithm. Finally, we use the algorithm developed in this paper to identify the source of a gas-leak (diffusing source) in a closed warehouse and also report some numerical results.

AB - We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect, we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms. We study the convergence behavior of the algorithm and provide sufficient conditions for its convergence. Our convergence result is rather general and contains as special cases the known convergence results for the incremental versions of the least-mean square algorithm. Finally, we use the algorithm developed in this paper to identify the source of a gas-leak (diffusing source) in a closed warehouse and also report some numerical results.

UR - http://www.scopus.com/inward/record.url?scp=56749168083&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56749168083&partnerID=8YFLogxK

U2 - 10.1109/ICIF.2008.4632232

DO - 10.1109/ICIF.2008.4632232

M3 - Conference contribution

AN - SCOPUS:56749168083

SN - 9783000248832

T3 - Proceedings of the 11th International Conference on Information Fusion, FUSION 2008

BT - Proceedings of the 11th International Conference on Information Fusion, FUSION 2008

T2 - 11th International Conference on Information Fusion, FUSION 2008

Y2 - 30 June 2008 through 3 July 2008

ER -