Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

Anthony Trezza, Donald J. Bucci, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density. The proposed truncation criterion is shown to have a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. This criterion is used to construct a Gibbs sampler that produces a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. A closed-form solution of the conditional sampling distribution assuming linear Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling. Multiple simulation results are provided to verify the efficacy of the truncation criterion, as well as the reduction in complexity.

Original languageEnglish (US)
Pages (from-to)1010-1025
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume70
DOIs
StatePublished - 2022

Keywords

  • Gibbs sampling
  • Random finite sets
  • measurement adaptive birth
  • state estimation
  • target tracking

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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