3 research outputs found
Learning-Based Distributed Detection-Estimation in Sensor Networks with Unknown Sensor Defects
We consider the problem of distributed estimation of an unknown deterministic
scalar parameter (the target signal) in a wireless sensor network (WSN), where
each sensor receives a single snapshot of the field. We assume that the
observation at each node randomly falls into one of two modes: a valid or an
invalid observation mode. Specifically, mode one corresponds to the desired
signal plus noise observation mode (\emph{valid}), and mode two corresponds to
the pure noise mode (\emph{invalid}) due to node defect or damage. With no
prior information on such local sensing modes, we introduce a learning-based
distributed procedure, called the mixed detection-estimation (MDE) algorithm,
based on iterative closed-loop interactions between mode learning (detection)
and target estimation. The online learning step re-assesses the validity of the
local observations at each iteration, thus refining the ongoing estimation
update process. The convergence of the MDE algorithm is established
analytically. Asymptotic analysis shows that, in the high signal-to-noise ratio
(SNR) regime, the MDE estimation error converges to that of an ideal
(centralized) estimator with perfect information about the node sensing modes.
This is in contrast to the estimation performance of a naive average consensus
based distributed estimator (without mode learning), whose estimation error
blows up with an increasing SNR.Comment: 15 pages, 2 figures, submitted to TS