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New Prediction for Extended Targets With Random Matrices
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Date
2014-04-01
Author
Granstrom, Karl
Orguner, Umut
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Electrical and Electronic Engineering
,
Aerospace Engineering
URI
https://hdl.handle.net/11511/40488
Journal
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
DOI
https://doi.org/10.1109/taes.2014.120211
Collections
Department of Electrical and Electronics Engineering, Article