State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

Geethu Joseph, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

3 Scopus citations

Abstract

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the Markov chain models intermittent outages due to communication channels and occlusions corresponding to moving objects. We rely on the sparse Bayesian learning framework to derive an estimation algorithm that uses Kalman smoothing to handle temporal correlation and the Viterbi algorithm to handle missing data. Further, we demonstrate the utility of our algorithm by applying it to the frequency division duplexed multiple input multiple output downlink channel estimation problem.

Original languageEnglish (US)
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages837-841
Number of pages5
ISBN (Electronic)9789082797091
DOIs
StatePublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: Aug 29 2022Sep 2 2022

Publication series

NameEuropean Signal Processing Conference
Volume2022-August
ISSN (Print)2219-5491

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
Country/TerritorySerbia
CityBelgrade
Period8/29/229/2/22

Keywords

  • Compressed sensing
  • FDD MIMO channel estimation
  • Viterbi algorithm
  • intermittent observations
  • missing data
  • sparse Bayesian learning
  • sparsity

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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