Name
Title | ||
|---|---|---|
A Student's t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers. |
Abstract | ||
|---|---|---|
In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student's tmixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student's t distribution as well as approximates the multi-target intensity as a mixture of Student's t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student's t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student's t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18041095 | SENSORS |
Keywords | Field | DocType |
multi-target tracking,PHD filter,Student's t mixture,outliers,robustness | Probability hypothesis density filter,Multi target tracking,Outlier,Process noise,Algorithm,Electronic engineering,Robustness (computer science),Engineering,Recursion,T distribution | Journal |
Volume | Issue | ISSN |
18 | 4.0 | 1424-8220 |
Citations | PageRank | References |
4 | 0.41 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuowei Liu | 1 | 4 | 0.75 |
| Shuxin Chen | 2 | 5 | 2.79 |
| Hao Wu | 3 | 5 | 1.78 |
| Renke He | 4 | 5 | 1.78 |
| Lin Hao | 5 | 4 | 1.76 |