Name
Title | ||
|---|---|---|
Robust Student's t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking With Heavy-Tailed Noises. |
Abstract | ||
|---|---|---|
In order to improve filtering accuracy and restrain the degradation of filtering performance caused by the heavy-tailed process and measurement noises in multi-target tracking, this paper proposes a robust Student's t mixture probability hypothesis density (PHD) filter. In the proposed method, a Student's t mixture is implemented to the PHD filter, which recursively propagates the intensity as a mixture of Student's t components in PHD filtering framework. Furthermore, with the advantage of a designed judging and re-weighting mechanism, an M-estimation-based dual-gating strategy is designed for the Student's t mixture implementation to suppress the negative effect of the heavy-tailed noises. Our proposed approach not only utilizes the Student's t distribution to match the real heavy-tailed non-Gaussian noise well but also enhances the robustness of the Student's t mixture-based approach via the designed dual-gating strategy. The simulation results verify that the proposed algorithm can keep good filtering accuracy in the presence of the process and measurement outliers simultaneously. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2856847 | IEEE ACCESS |
Keywords | Field | DocType |
Multi-target tracking,PHD filter,student's t mixture,heavy-tailed noises,dual-gating strategy,robustness | Probability hypothesis density filter,Multi target tracking,Noise measurement,Computer science,Filter (signal processing),Outlier,Algorithm,Robustness (computer science),Probability density function,Recursion,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuowei Liu | 1 | 4 | 0.75 |
| Shuxin Chen | 2 | 5 | 2.79 |
| Hao Wu | 3 | 5 | 1.78 |
| Kun Chen | 4 | 16 | 8.01 |