The Gaussian Mixture Probability Hypothesis Density Filter

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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

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