Name
Abstract | ||
|---|---|---|
Gaussian mixture probability hypothesis density (GM-PHD) recursion is a promising, computationally tractable implementation for the probability hypothesis density (PHD) filter. The competitive GM-PHD (CGM-PHD) and penalized GM-PHD (PGM-PHD) filters employ renormalization schemes to refine the weights assigned to each target and improve the estimation performance of the GM-PHD filter for closely spaced targets. However, these methods do not provide target trajectories over time, and the problem of wrongly identifying close targets is not still solved for the GM-PHD tracker. In this paper, we propose a collaborative penalized scheme to overcome the drawbacks of the GM-PHD tracker using the track label of each Gaussian component in the GM-PHD recursion. The simulation results show that the collaborative penalized GM-PHD (CPGM-PHD) tracker not only improves the estimation accuracy of the number of target and states but also provides the correct identities of targets in close proximity. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.sigpro.2014.01.034 | Signal Processing |
Keywords | Field | DocType |
Multiple target tracking,Probability hypothesis density,Gaussian mixture PHD | Renormalization,Probability hypothesis density filter,Pattern recognition,Gaussian,Artificial intelligence,Mathematics,Recursion | Journal |
Volume | Issue | ISSN |
102 | null | 0165-1684 |
Citations | PageRank | References |
7 | 0.66 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wang | 1 | 183 | 62.13 |
| Huadong Meng | 2 | 175 | 20.65 |
| Yimin Liu | 3 | 158 | 25.46 |
| Xiqin Wang | 4 | 290 | 33.88 |