Labeled Multi-Bernoulli Filter based Group Target Tracking Using SDE and Graph Theory
- Resource Type
- Conference
- Authors
- Li, Li; Wu, Qinchen; Yang, Bin; Wei, Shaoming; Wang, Jun
- Source
- 2021 IEEE 24th International Conference on Information Fusion (FUSION) Information Fusion (FUSION), 2021 IEEE 24th International Conference on. :1-8 Nov, 2021
- Subject
- Aerospace
Computing and Processing
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Target tracking
Predictive models
Filtering algorithms
Prediction algorithms
Information filters
Filtering theory
Mathematical models
group target tracking
stochastic differential equation
Random Finite set (RFS)
LMB filter
graph theory
- Language
Multi-target tracking is an extremely challenging task when targets move in the formation of groups and interact with each other. Group target tracking has to deal with this problem in contrast to independently moving targets as assumed in most multi-target tracking algorithms. A feasible approach for group target tracking is to estimate the group structure and modify the motion model in the prediction step of multi-target tracker according to the group structure. In this paper, we propose an ad hoc labeled multi-Bernoulli (LMB) filter for tracking group target with interaction, which use stochastic differential equation to model the joint motion of group targets and estimate group structure by using graph theory. Simulation results show that the proposed algorithm can estimate the target state more accurately than the traditional method without group motion modification.