The Gaussian Mixture Probability Hypothesis Density Filter

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors B. Vo, W. Ma
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract The probability hypothesis density filter has attracted increasing interest since Mahler first introduced it in 2000. This paper proposes an improved merging algorithm for the Gaussian mixture probability hypothesis density filter, which can track closely proximity targets. The proposed algorithm utilizes not only the Gaussian components' means and covariance, but their weights as a new criterion to improve the conventional pruning algorithm's estimate precision. Simulation results demonstrate that this improved algorithm is more robust and easier to implement than the formal one.
Date of publication 2017
Code Programming Language Matlab
Comment

Copyright Researcher 2022