Name
Abstract | ||
|---|---|---|
In Bayesian multi-target tracking (MTT), knowledge of clutter intensity is required for effective multi-target state estimation. In this paper, we propose an online multi-target filter that can operate under background with unknown clutter intensity. Our solution is based on the Poisson multi-Bernoulli mixture (PMBM) filter that jointly estimating the multi-target state and clutter rate. The unknown clutter rate is modeled as Gamma distribution, consequently, the derived PMBM recursion that adapts for unknown clutter intensity remains closed. Moreover, we adopt a Gibbs sampler to find the finite number of global hypotheses, then the multi-Bernoulli mixture is approximated by a multi-Bernoulli distribution based on a simple fusion strategy. The derived Poison multi-Bernoulli (PMB) filter has a similar form with labeled multi-Bernoulli filter (LMB) but has a straightforward prediction step. Simulations conducted for linear-Gaussian models are presented to verify that the proposed algorithm can adapt to the background with unknown clutter intensity and yield reliable tracking performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2936864 | IEEE ACCESS |
Keywords | DocType | Volume |
Multi-target tracking (MTT),random finite set (RFS),Poisson multi-Bernoulli (PMB),Gibbs sampler,online parameter estimation | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijian Si | 1 | 44 | 11.34 |
| Hongfan Zhu | 2 | 0 | 0.34 |
| Zhiyu Qu | 3 | 12 | 4.68 |