A novel distributed fusion algorithm for multi-sensor nonlinear tracking
A novel distributed fusion algorithm for multi-sensor nonlinear tracking
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DOI：
10.1186/s13634-016-0362-y
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发表时间：
2016-05
影响因子：
1.9
通讯作者：
Jingxian Liu;Zulin Wang;Mai Xu
中科院分区：
文献类型：
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作者：
Jingxian Liu;Zulin Wang;Mai Xu
协方差交（CI），特别是具有反馈结构的协方差交可以很容易地与非线性滤波器相结合，解决多传感器非线性跟踪的分布式融合问题。然而，本文证明了CI算法是次优的，从而降低了融合精度。为了避免这一问题，提出了一种新的分布式融合算法，即蒙特卡罗贝叶斯（MCB）算法。首先，构建了基于贝叶斯跟踪框架的分布式融合体系结构。然后，将蒙特卡罗采样纳入该体系结构，形成一种可行的非线性跟踪解决方案。最后，仿真结果验证了该算法在非线性跟踪中的分布式融合性能。
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.