Name
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 jitter 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 Gaussians 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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICIF.2006.301809 | 2006 9TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4 |
Keywords | Field | DocType |
tracking, data association, filtering, PHD, filter, random sets | Radar tracker,False alarm,Computer science,Control theory,Artificial intelligence,Gaussian process,Trajectory,Computer vision,Clutter,Algorithm,Filter (signal processing),Gaussian,Gaussian noise | Conference |
Citations | PageRank | References |
32 | 4.51 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel E. Clark | 1 | 360 | 36.76 |
| Kusha Panta | 2 | 52 | 7.88 |
| Ba-Ngu Vo | 3 | 2408 | 175.90 |