Title

Bayesian Cramér-Rao Bound For Distributed Vector Estimation With Linear Observation Model

Abstract

In this paper we study the problem of distributed estimation of a random vector in wireless sensor networks (WSN) with linear observation model. Each sensor makes a noisy observation, quantizes its observation, maps it to a digitally modulated symbol, and transmits the symbol over erroneous wireless channels (subject to fading and noise) to a fusion center (FC), which is tasked with fusing the received signals and 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 system parameters, including 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

6-25-2014

Publication Title

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Volume

2014-June

Number of Pages

712-716

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/PIMRC.2014.7136257

Socpus ID

84944326712 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84944326712

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