TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Schuster, Michael
A1  - Reuter, Johannes
A1  - Wanielik, Gerd
T1  - Multi detection joint integrated probabilistic data association using random matrices with applications to radar-based multi object tracking
JF  - Journal of Advances in Information Fusion
N2  - In extended object tracking, a target is capable to generate more than one measurement per scan. Assuming the target being of elliptical shape and given a point cloud of measurements, the Random Matrix Framework can be applied to concurrently estimate the target’s dynamic state and extension. If the point cloud contains also clutter measurements or origins from more than one target, the data association problem has to be solved as well. However, the well-known joint probabilistic data association method assumes that a target can generate at most one detection. In this article, this constraint is relaxed, and a multi-detection version of the joint integrated probabilistic data association is proposed. The data association method is then combined with the Random Matrix framework to track targets with elliptical shape. The final filter is evaluated in the context of tracking smaller vessels using a high resolution radar sensor. The performance of the filter is shown in simulation and in several experiments.
Y1  - 2017
UR  - http://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol12_2_Multi%20Detection%20Joint.pdf
SN  - 1557-6418
SS  - 1557-6418
VL  - 12
IS  - 2
SP  - 175
EP  - 188
ER  -