Abstract
The Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms. The initial implementation of the GM-PHD filter provided estimates for the set of target states at each point in time but did not ensure continuity of the individual target tracks. It is shown here that the trajectories of the targets can be determined directly from the evolution of the Gaussian mixture and that single Gaussions within this mixture accurately track the correct targets. Furthermore, the technique is demonstrated to be successful in estimating the correct number of targets and their trajectories in high clutter density and shows better performance than the MHT filter.
Original language | English |
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Title of host publication | 2006 9th International Conference on Information Fusion, FUSION |
DOIs | |
Publication status | Published - 2006 |
Event | 2006 9th International Conference on Information Fusion - Florence, Italy Duration: 10 Jul 2006 → 13 Jul 2006 |
Conference
Conference | 2006 9th International Conference on Information Fusion |
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Abbreviated title | FUSION |
Country/Territory | Italy |
City | Florence |
Period | 10/07/06 → 13/07/06 |
Keywords
- Data association
- Filtering
- PHD filter
- Random sets
- Tracking