Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer

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

Research output: Contribution to journalArticlepeer-review

Abstract

Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers are often based on the ell-p-norm which cannot account for important geometrical relationships between neighboring signal elements. We propose a practical approach to using the earth mover's distance (EMD) via the earth mover's distance dynamic filtering (EMD-DF) algorithm for causally tracking time-varying sparse signals when there is a natural geometry to the coefficient space that should be respected (e.g., meaningful ordering). Specifically, this letter presents a new Beckmann formulation that dramatically reduces computational complexity, as well as an evaluation of the performance and complexity of the proposed approach in imaging and frequency tracking applications with real and simulated neurophysiology data.

Original languageEnglish (US)
Article number9115856
Pages (from-to)1120-1124
Number of pages5
JournalIEEE Signal Processing Letters
Volume27
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Online tracking
  • dynamic filtering
  • earth mover's distance
  • optimal transport

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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