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
Y1 - 2008
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.
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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 -