Probabilistic Labeling in Radar Track-before-Detect Processing: Algorithms for tracking closely-spaced and/or interacting targets

Research output: ThesisDissertation (TU Delft)

33 Downloads (Pure)

Abstract

Radar-tracking of low-observable targets such as drones suffers from low detection performance. In these type of applications, it is desirable to avoid data thresholding in order to preserve the weak target signal in the raw sensor data. This thesis considers the Multiple Object Tracking (MOT) problem in the context of radar Track-before-Detect (TrBD) processing, where the raw radar data is fed into the filtering process without previous compression into a finite set of detection/plots....
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Yarovoy , Alexander, Supervisor
  • Driessen, J.N., Advisor
Award date27 Nov 2023
Print ISBNs978-94-6384-512-0
DOIs
Publication statusPublished - 2023

Keywords

  • Multiple target tracking
  • Radar Track-before-detect
  • Bayesian Inference
  • Tracking of interacting/closely-spaced/unresolved targets
  • non-linear filtering
  • detection of target anomalous behaviour
  • Particle filtering
  • data-association free tracking
  • Sequential Monte Carlo methods

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