A New Prediction for Extended Targets with Random Matrices
2012 (English)Manuscript (preprint) (Other academic)
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 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.
Place, publisher, year, edition, pages
Signal Processing Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-82004OAI: oai:DiVA.org:liu-82004DiVA: diva2:557429