Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of the distributed multi-target tracking with labeled set filters in the framework of Generalized Covariance Intersection (GCI). Our analyses show that the space mismatching phenomenon, which means the same realization drawn from spaces of different sensors does not have the same implication, is quite common in practical scenarios and may bring serious problems. Our contributions are two-fold. Firstly, we provide a principled mathematical definition of label spaces matching (LS-DM) based on information divergence, which is also referred to as LS-M criterion. Then, to handle the LS-DM, we propose a novel two-step distributed fusion algorithm, named as GCI fusion via spaces matching (GCI-LSM). The first step is to match the spaces from different sensors. To this end, we build a ranked assignment problem and design a cost function consistent with LS-M criterion to seek the optimal solution of matching correspondence between spaces of different sensors. The second step is to perform the GCI fusion on the matched space. We also derive the GCI fusion with generic labeled multi-object (LMO) densities based on LS-M, which is the foundation of labeled distributed fusion algorithms. Simulation results for Gaussian mixture implementation highlight the performance of the proposed GCI-LSM algorithm in two different tracking scenarios. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Systems and Control | Data mining,Mathematical optimization,Ranking,Fusion,Covariance intersection,Algorithm,Assignment problem,Gaussian,Kullback–Leibler divergence,Mathematics |
DocType | Volume | Citations |
Journal | abs/1603.08336 | 1 |
PageRank | References | Authors |
0.36 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bailu wang | 1 | 37 | 3.92 |
| Wei Yi | 2 | 252 | 36.97 |
| suqi li | 3 | 37 | 3.92 |
| Lingjiang Kong | 4 | 346 | 38.93 |
| Xiaobo Yang | 5 | 118 | 20.61 |