TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Hoher, Patrick
A1  - Reuter, Johannes
A1  - Govaers, Felix
A1  - Koch, Wolfgang
T1  - Extended Object Tracking and Shape Classification using Random Matrices and Virtual Measurement Models
T2  - IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration for Intelligent Systems (SDF-MFI), 27-29 Nov 2023, Bonn, Germany
N2  - 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.
KW  - Extended object tracking
KW  - Random matrices
KW  - Reference model
KW  - Extent estimation
KW  - Shape classification
KW  - Virtual measurement model
Y1  - 2023
SN  - 979-8-3503-8258-7
SB  - 979-8-3503-8258-7
SN  - 979-8-3503-8259-4
SB  - 979-8-3503-8259-4
U6  - https://doi.org/10.1109/SDF-MFI59545.2023.10361348
DO  - https://doi.org/10.1109/SDF-MFI59545.2023.10361348
SP  - 8
S1  - 8
PB  - IEEE
ER  -