Sensor Selection And Power Allocation Via Maximizing Bayesian Fisher Information For Distributed Vector Estimation

Keywords

Bayesian Fisher Information Matrix; Distributed estimation; linear observation model; multiple-choice Knapsack problem; power allocation; sensor selection

Abstract

In this paper we study the problem of distributed estimation of a Gaussian vector with linear observation model in a wireless sensor network (WSN) consisting of K sensors that transmit their modulated quantized observations over orthogonal erroneous wireless channels (subject to fading and noise) to a fusion center, which estimates the unknown vector. Due to limited network transmit power, only a subset of sensors can be active at each task period. Here, we formulate the problem of sensor selection and transmit power allocation that maximizes the trace of Bayesian Fisher Information Matrix (FIM) under network transmit power constraint, and propose three algorithms to solve it. Simulation results demonstarte the superiority of these algorithms compared to the algorithm that uniformly allocates power among all sensors.

Publication Date

4-10-2018

Publication Title

Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

Volume

2017-October

Number of Pages

1379-1383

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACSSC.2017.8335580

Socpus ID

85050989305 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050989305

This document is currently not available here.

Share

COinS