New Prediction for Extended Targets With Random Matrices

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

Collections

Department of Electrical and Electronics Engineering, Article