Probabilistic Labeling in Radar Track-before-Detect Processing

Algorithms for tracking closely-spaced and/or interacting targets

Doctoral thesis (2023)

Authors

C. Moreno León Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2023 C. Moreno León
DOI
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https://doi.org/10.4233/uuid:039445ea-ce0e-445d-b07c-5e369fe7e708
Bayesian Inference Multiple target tracking Radar Track-before-detect 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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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

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....