Noise Enhanced Distributed Bayesian Estimation

Abstract

In this paper we consider distributed estimation of an unknown Gaussian random variable with known mean and variance, where each sensor observation is affected by both multiplicative and additive Gaussian observation noises. We derive the corresponding Cramer Rao Lower Bound (CRLB) for both quantized and full precision observations. In sequel we provide some closed-form approximations for both CRLB expressions which provide us with better understanding of behavior of CRLBs. Afterwards through analytic and simulation results we report some scenarios that multiplicative observation noise can play an enhancive role in terms of estimation accuracy. We call this phenomena enhancement mode of multiplicative noise.

Publication Date

6-16-2017

Publication Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Number of Pages

4217-4221

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICASSP.2017.7952951

Socpus ID

85023755230 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85023755230

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