TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Hoher, Patrick
A1  - Wirtensohn, Stefan
A1  - Baur, Tim
A1  - Reuter, Johannes
A1  - Govaers, Felix
A1  - Koch, Wolfgang
T1  - Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
JF  - IEEE Transactions on Signal Processing
N2  - 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.
KW  - Extended object tracking
KW  - Random matrices
KW  - Lidar
KW  - Reference model
KW  - Extension estimation
Y1  - 2022
SN  - 1941-0476
SS  - 1941-0476
SN  - 1053-587X
SS  - 1053-587X
U6  - https://doi.org/10.1109/TSP.2021.3138006
DO  - https://doi.org/10.1109/TSP.2021.3138006
N1  - Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich
VL  - 2022
IS  - Vol. 70
SP  - 228
EP  - 239
PB  - IEEE
ER  -