Sparse Dynamic Filtering via Earth Mover's Distance Regularization

Nicholas P. Bertrand, John Lee, Adam S. Charles, Pavel Dunn, Christopher J. Rozell

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

Abstract

Tracking time-varying signals is an important task for practical systems working with large discretized domains. Under such settings, sparsity-based approaches improve tracking accuracy since typically few targets appear in the scene (i.e. few locations in the discretized space are occupied). Discretization introduces a unique challenge: the traditional Φ{p}-norm dynamic constraints produce significant errors when there is even a small spatial mismatch between the predicted and true state. To overcome this, we present a tracking algorithm leveraging concepts from optimal transport, namely utilizing the earth-movers distance (EMD) as a dynamic regularizer to the Φ{1}-regularized inference problem (i.e., LASSO [1], or BPDN [2]). We extend the problem formulation to complex valued signals and modify the optimization program to reduce the computational burden. We demonstrate the efficacy of our approach in imaging and frequency tracking applications.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4334-4338
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Externally publishedYes
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period4/15/184/20/18

Keywords

  • Compressive Sensing
  • Dynamic Filtering
  • Earth-mover's Distance
  • Kalman Filtering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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