Earth-Mover's distance as a tracking regularizer

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

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

Abstract

Tracking time-varying signals is an important part of many engineering systems. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. Leveraging sparsity, however, depends heavily on gridding the space, treating the signal as a collection of active or inactive pixels in an image, rather than traditional methods which track the continuous spatial coordinates. Using the dynamics constraint in this setting is challenging, as a model which approximately predicts target location may result in seemingly large errors, as measured by the ℓp-norm typically used in such algorithms. To take advantage of approximate spatial priors without introducing unnecessary penalties, we present a tracking algorithm using the earth-mover's distance (EMD) as an alternate dynamics regularization term. We note that while requiring a higher computational burden, the EMD can more effectively utilize target location prediction when the space is gridded.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Externally publishedYes
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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