TY - CHAP U1 - Konferenzveröffentlichung A1 - Böhler, Julian A1 - Baur, Tim A1 - Wirtensohn, Stefan A1 - Reuter, Johannes T1 - Stochastic partitioning for extended object probability hypothesis density filters T2 - Sensor Data Fusion: Trends, Solutions, Applications (SDF), 15-17 Oct. 2019, Bonn, Germany N2 - This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements. This can lead to poor filter performance if the tracked extended objects are closely spaced. The proposed method called Stochastic Partitioning (StP) is based on sampling methods and was inspired by a former work of Granström et. al. In this work, the StP method is applied to a Gaussian inverse Wishart (GIW) PHD filter and compared to a second filter implementation that uses the heuristic Distance Partitioning (DP) method. The performance is evaluated in Monte Carlo simulations in a scenario where two objects approach each other. It is shown that the sampling based StP method leads to an improved filter performance compared to DP. KW - Partitioning algorithms KW - Sampling methods KW - Object tracking KW - Radar tracking KW - Monte Carlo methods Y1 - 2019 SN - 978-1-7281-5085-7 SB - 978-1-7281-5085-7 U6 - https://doi.org/10.1109/SDF.2019.8916656 DO - https://doi.org/10.1109/SDF.2019.8916656 N1 - Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich SP - 1 EP - 6 PB - IEEE ER -