Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
- Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.
Author: | Patrick HoherORCiD, Stefan WirtensohnORCiD, Tim BaurORCiD, Johannes ReuterORCiD, Felix GovaersORCiD, Wolfgang Koch |
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DOI: | https://doi.org/10.1109/TSP.2021.3138006 |
ISSN: | 1941-0476 |
ISSN: | 1053-587X |
Parent Title (English): | IEEE Transactions on Signal Processing |
Volume: | 2022 |
Publisher: | IEEE |
Document Type: | Article |
Language: | English |
Year of Publication: | 2022 |
Release Date: | 2022/08/11 |
Tag: | Extended object tracking; Random matrices; Lidar; Reference model; Extension estimation |
Issue: | Vol. 70 |
First Page: | 228 |
Last Page: | 239 |
Note: | Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich |
Institutes: | Institut für Systemdynamik - ISD |
Relevance: | Peer reviewed Publikation in Master Journal List |
Open Access?: | Nein |
Licence (German): | Urheberrechtlich geschützt |