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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.

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Metadaten
Author:Patrick HoherORCiD, Stefan WirtensohnORCiD, Tim BaurORCiD, Johannes ReuterORCiD, Felix GovaersORCiD, Wolfgang Koch
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 (English):License LogoLizenzbedingungen IEEE