Computer Science – Multiagent Systems
Scientific paper
2008-08-29
Computer Science
Multiagent Systems
Submitted for publication, 51 pages
Scientific paper
The paper studies the problem of distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and imperfect inter-sensor communication. We introduce the concept of \emph{separably estimable} observation models, which generalize the observability condition for linear centralized estimation to nonlinear distributed estimation. We study the algorithms $\mathcal{NU}$ (with its linear counterpart $\mathcal{LU}$) and $\mathcal{NLU}$ for distributed estimation in separably estimable models. We prove consistency (all sensors reach consensus almost sure and converge to the true parameter value), asymptotic unbiasedness and asymptotic normality of these algorithms. Both the algorithms are characterized by appropriately chosen decaying weight sequences in the estimate update rule. While the algorithm $\mathcal{NU}$ is analyzed in the framework of stochastic approximation theory, the algorithm $\mathcal{NLU}$ exhibits mixed time-scale behavior and biased perturbations and require a different approach, which we develop in the paper.
Kar Soummya
Moura Jose M. F.
Ramanan Kavita
No associations
LandOfFree
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-613324