Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications

Research output: Contribution to journalArticlepeer-review


This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization-to the nonlinear non-Gaussian case-of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.

Original languageEnglish (US)
Article number030905
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Issue number3
StatePublished - Mar 1 2018

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications


Dive into the research topics of 'Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications'. Together they form a unique fingerprint.

Cite this