Posterior distribution preprocessing with the JPDA algorithm: PACsim data set

This paper presents tracking results on the PACsim data set using a framework based on the JPDA algorithm with a posterior distribution preprocessing step. The dataset is a multistatic simulation designed to approximate real-life data. In this paper, we extend the posterior distribution preprocessing technique to include feature data and compare tracking results with and without feature information. Results show that the inclusion of feature data in the preprocessing stage can improve tracking performance. This work also explores the benefits of more extensive parameter tuning for the harder tracking scenarios included in the dataset.