A novel distributed fusion algorithm for multi-sensor nonlinear tracking

作者：Liu, Jingxian; Wang, Zulin; Xu, Mai^*

来源：EURASIP Journal on Advances in Signal Processing, 2016, 2016(1): 62.

DOI：10.1186/s13634-016-0362-y

• Bayesian tracking framework
• Monte Carlo sampling
• Covariance intersection

The covariance intersection (CI), especially with feedback structure, can be easily combined with nonlinear filters to solve the distributed fusion problem of multi-sensor nonlinear tracking. However, this paper proves that the CI algorithm is sub-optimal, thus degrading the fusion accuracy. To avoid such an issue, a novel distributed fusion algorithm, namely Monte Carlo Bayesian (MCB) algorithm, is proposed. First, it builds a distributed fusion architecture based on the Bayesian tracking framework. Then, the Monte Carlo sampling is incorporated into this architecture to form a feasible solution to nonlinear tracking. Finally, the simulation results verify that our MCB algorithm advances the state-of-the-art distributed fusion of nonlinear tracking.

• 出版日期2016-5-18
• 单位北京航空航天大学