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Extended Object Tracking and Shape Classification using Random Matrices and Virtual Measurement Models

  • The random matrix approach is a robust algorithm to filter the mean and covariance matrix of noisy observations of a dynamic object. Afterward, virtual measurement models can be used to find iteratively the extent parameters of an object that would cause the same statistical moments within their measurements. In previous work, this was limited to elliptical targets and only contour measurements.In this paper, we introduce the parallel use of an elliptical, triangular and rectangular-shaped virtual measurement model and a shape classification that selects the model that fits best to the measurements. The measurement likelihood is modeled either via ray tracing, a uniformly or normally spatial distribution over the object’s extent or as a combination of those.The results show that the extent estimation works precisely and that the classification accuracy highly depends on the measurement noise.

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Author:Patrick HoherORCiD, Johannes ReuterORCiD, Felix GovaersORCiD, Wolfgang Koch
Parent Title (English):IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration for Intelligent Systems (SDF-MFI), 27-29 Nov 2023, Bonn, Germany
Document Type:Conference Proceeding
Year of Publication:2023
Release Date:2024/01/23
Tag:Extended object tracking; Random matrices; Reference model; Extent estimation; Shape classification; Virtual measurement model
Page Number:8
Institutes:Institut für Systemdynamik - ISD
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt