Name
Abstract | ||
|---|---|---|
The main drawback of probability hypothesis density (PHD) filter is that it can't identify the trajectories of the different targets. Data association for PHD filter based on Multiple Hypotheses Tracking (MHT) is presented to solve the problem. The track-oriented MHT is used to perform data association on the output of PHD filter. An adaptive Kalman filter based on "current" statistic model, combined with MHT, is implemented to track maneuvering targets. Two examples are given to test the performance of the new method. Monte Carlo simulation results show that this approach is computationally feasible and effective for associating multi-targets in dense clutter environments. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICIF.2008.4632445 | Fusion |
Keywords | Field | DocType |
data association,probability hypothesis density,track-oriented mht,probability,monte carlo methods,monte carlo simulation,kalman filters,sensor fusion,adaptive filters,statistical model,kalman filter,statistical analysis | Probability hypothesis density filter,Computer vision,Monte Carlo method,Statistic,Clutter,Computer science,Kalman filter,Sensor fusion,Data association,Artificial intelligence,Adaptive filter | Conference |
Volume | Issue | Citations |
null | null | 4 |
PageRank | References | Authors |
0.62 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 394 | 61.44 |
| Zhongliang Jing | 2 | 351 | 39.38 |
| Shiqiang Hu | 3 | 56 | 6.96 |