Bayesian Nonparametric Modeling and Transfer Learning for Tracking under Measurement Noise Uncertainty

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

Abstract

We propose a method for object tracking under unknown and time-varying environmental conditions that incorporates transfer learning with Bayesian filtering and Bayesian nonparametric modeling. The main tracking task assumes that the sensor measurement noise characteristics are unknown and change with time. The characteristics are learned by incorporating knowledge that was previously acquired and stored by multiple sources tracking under similar varying conditions. We assume that each learning source models their own time-varying noise distribution using Dirichlet process mixtures whose parameters are learned using Bayesian nonparametric priors. The multiple models are transferred and combined to model variation in the main tracking task. Simulations demonstrate the improved tracking performance when compared to tracking without transferred knowledge.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages826-830
Number of pages5
ISBN (Electronic)9781665458283
DOIs
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period10/31/2111/3/21

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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