New Prediction for Extended Targets With Random Matrices

Download
2014-04-01
Granstrom, Karl
Orguner, Umut
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.

Citation Formats
K. Granstrom and U. Orguner, “New Prediction for Extended Targets With Random Matrices,” IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, vol. 50, no. 2, pp. 1577–1589, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40488.