Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Pranay Sharma, Augustin Alexandru Saucan, Donald J. Bucci, Pramod K. Varshney

Research output: Contribution to journalArticle

Abstract

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data association uncertainty, i.e., the associations between measurements and target tracks are unknown. Existing CS and tracking algorithms either make the assumption of no data association uncertainty or employ a hard-decision rule for measurement-to-target associations. We propose a novel decentralized SCS-MTT method for an unknown and time-varying number of targets under association uncertainty. Marginal posterior densities for agents and targets are obtained by an efficient belief propagation (BP) based scheme while data association is handled by marginalizing over all target-to-measurement association probabilities. Decentralized single Gaussian and Gaussian mixture implementations are provided based on average consensus schemes, which require communication only with one-hop neighbors. An additional novelty is a decentralized Gibbs mechanism for efficient evaluation of the product of Gaussian mixtures. Numerical experiments show the improved CS and MTT performance compared to the conventional approach of separate localization and target tracking.

Original languageEnglish (US)
Article number8864098
Pages (from-to)5896-5911
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume67
Issue number22
DOIs
StatePublished - Nov 15 2019

Keywords

  • Belief Propagation
  • Gibbs Sampling
  • Multi-target tracking
  • Self-localization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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