New prediction for extended targets with random matrices
Karl Granström, Umut Orguner · IEEE Transactions on Aerospace and Electronic Systems · 2014
This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.