Bayesian Cramér-Rao Bound For Distributed Estimation Of Correlated Data With Non-Linear Observation Model

Abstract

In this paper we study the problem of distributed estimation of a random vector in wireless sensor networks (WSNs) with non-linear observation model. Sensors transmit their binary modulated quantized observations over orthogonal erroneous wireless channels (subject to fading and noise) to a fusion center, which is tasked with estimating the unknown vector. We derive the Bayesian Cramer-Rao Bound (CRB) matrix and study the behavior of its trace (through analysis and simulations), with respect to the observation and communication channel signal-to-noise ratios (SNRs). The derived CRB serves as a benchmark for performance comparison of different Bayesian estimators, including linear MMSE estimator.

Publication Date

4-24-2015

Publication Title

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volume

2015-April

Number of Pages

1484-1488

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACSSC.2014.7094709

Socpus ID

84940571727 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84940571727

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