Distributed compression and maximum likelihood reconstruction of finite autocorrelation sequences

Aritra Konar, Nicholas D. Sidiropoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimating the autocorrelation of wide-sense stationary time series is important for a wide range of statistical signal processing and data analysis tasks. Distributed autocorrelation sensing strategies are of interest when multiple pieces or realizations of the time series are measured at different locations. This paper considers a distributed autocorrelation sensing scheme based on randomly filtered power measurements, each compressed down to one bit, without any sensor coordination. A Maximum Likelihood (ML) reconstruction scheme is proposed and is shown to work well, even at high overall compression ratios and with a substantial fraction of bit errors. Whereas the ML formulation appears non-convex, it is proven that it in fact possesses hidden convexity, which enables optimal solution. Simulations are used to illustrate the performance of the proposed ML approach.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages513-517
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

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