Network parameter estimation with detection failures

Distributed sensing systems fuse information from various local measurements to form joint estimates of physical phenomena. We study Bayesian parameter estimation with measurement failures, where the probability of failing to make an observation depends on the unknown parameters. Both the absence and the existence of the measurements provide information about the parameters. We present lower bounds for minimum mean square error estimation. The bounds are applied to the analysis of sensor arrays for source localization and the distributed estimation problem with binary detectors.