Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
Extended Object Tracking Using Monte Carlo Methods
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
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In an extended target PHD filter, the exact filter requires all possible partitions of the current measurement set for updating, which is computationally intractable. In order to limit the number of partitions, a fast partitioning algorithm for extended target Gaussian mixture PHD (ET-GM-PHD) filter is proposed, which substitutes Distance Partitioning with a fuzzy ART model. Alternative partitions of the measurement set are generated by the different vigilance values in ART. Suitable measures and remedies are given to handle the problems arisen by overestimation of target number and spatially close targets. The simulation results show that the proposed algorithm can well handle the close-spaced targets and obviously reduce computational burden without losing tracking performance, which implies good application prospects for the real-time extended target tracking system.