Sparsity penalties in dynamical system estimation

Adam Charles, M. Salman Asif, Justin Romberg, Christopher Rozell

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

Abstract

In this work we address the problem of state estimation in dynamical systems using recent developments in compressive sensing and sparse approximation. We formulate the traditional Kalman filter as a one-step update optimization procedure which leads us to a more unified framework, useful for incorporating sparsity constraints. We introduce three combinations of two sparsity conditions (sparsity in the state and sparsity in the innovations) and write recursive optimization programs to estimate the state for each model. This paper is meant as an overview of different methods for incorporating sparsity into the dynamic model, a presentation of algorithms that unify the support and coefficient estimation, and a demonstration that these suboptimal schemes can actually show some performance improvements (either in estimation error or convergence time) over standard optimal methods that use an impoverished model.

Original languageEnglish (US)
Title of host publication2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 45th Annual Conference on Information Sciences and Systems, CISS 2011 - Baltimore, MD, United States
Duration: Mar 23 2011Mar 25 2011

Publication series

Name2011 45th Annual Conference on Information Sciences and Systems, CISS 2011

Other

Other2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
Country/TerritoryUnited States
CityBaltimore, MD
Period3/23/113/25/11

Keywords

  • Compressive Sensing
  • Dynamical Systems
  • State Estimation

ASJC Scopus subject areas

  • Information Systems

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