From 4D Radar Point-Cloud to State Estimation: A Comparison of Algorithms
R. van der Voort (TU Delft - Mechanical Engineering)
Robert Babuska – Mentor (TU Delft - Learning & Autonomous Control)
A. Tasoglou – Graduation committee member (Damen Research, Development and Innovation B.V.)
Holger Caesar – Graduation committee member (TU Delft - Intelligent Vehicles)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This thesis presents a comparative analysis of multi-target tracking algorithms for estimating the kinematic state and physical extent of maritime vessels from 4D millimetre-wave radar point clouds. The research addresses a gap in understanding the trade-offs between algorithmic complexity and motion model fidelity for inland maritime navigation.
The study evaluates three tracking paradigms, Tracking-by-Detection (TBD), the Probability Hypothesis Density (PHD) filter, and the Poisson Multi-Bernoulli Mixture (PMBM) filter, each tested with linear (Constant Velocity, Constant Acceleration) and non-linear (Constant Turn Rate) motion models. To represent vessel shape, three extended object models were implemented: the Random Matrix, Star-Convex Gaussian Process, and Principal Axis models.
Performance was evaluated using a mix of simulated datasets with perfect ground truth and collected real-world datasets. A hyperparameter optimisation process was conducted for every algorithm combination using the Optuna framework, with the objective of minimising the Generalised Optimal Sub-Pattern Assignment metric using a specified cost function.
The results yielded several key, and at times counterintuitive, findings. Firstly, increased algorithmic complexity did not yield superior performance. The simpler TBD and PHD frameworks consistently outperformed the theoretically optimal but computationally intensive PMBM filter, which was prone to generating unstable tracks and excessive false positives. Secondly, non-linear motion models offered no significant advantage over the simpler linear models. This could be attributed to the slow dynamics of maritime vessels, the high sensor update rate, which makes linear extrapolation sufficient, and the fact that measurement noise often dominates any subtle gains from a more precise motion model. Thirdly, for extent estimation, the Random Matrix model demonstrated the best balance of accuracy, stability, and computational efficiency. The more complex Gaussian Process and Principal Axis models struggled with stability and practicality.
The study concludes that the optimal balance is achieved by pairing simple linear motion models with the Random Matrix extent model, yielding accurate cardinality estimation, reliable state tracking, and efficient computation. The findings underscore that robustness and interpretability outweigh algorithmic sophistication when designing maritime tracking systems based on 4D radar. Ultimately, the thesis establishes a principled performance baseline and highlights the diminishing returns of complexity in this domain.