Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
Artikel i vetenskaplig tidskrift, 2021

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.


Density measurement


extended object tracking

Automotive engineering

Computational modeling

autonomous driving

Radar measurements

Automotive radar

Time measurement


random matrix


Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Pu Wang

Mitsubishi Electric Research Laboratories

Karl Berntorp

Mitsubishi Electric Research Laboratories

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Hassan Mansour

Mitsubishi Electric Research Laboratories

Petros Boufounos

Mitsubishi Electric Research Laboratories

Philip V. Orlik

Mitsubishi Electric Research Laboratories

IEEE Journal on Selected Topics in Signal Processing

1932-4553 (ISSN)

Vol. 15 4 1013-1029



Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)



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