基于多传感器协同的自适应联邦滤波跟踪算法

Adaptive Federated Filtering Tracking Algorithm Based on Multi-Sensor Redundant Data

为了充分利用多传感器的冗余信息实现高精度跟踪,提出了一种带有离群点检测的冗余信息自适应联邦滤波跟踪算法。首先,在信息分配阶段,针对冗余信息设计了一种自适应信息分配因子,提高了信息分配效率;其次,在信息融合阶段,为了降低误差数据对跟踪结果的影响,提出了一种离群点检测算法,针对存在相关性且服从高斯分布的数据,通过D-S证据理论综合所有滤波器的判断评估数据是否为离群数据;最后,使用线性最小方差估计进行融合,得到更为精确的最终估计结果。仿真验证了所提算法具有更好的跟踪精度和鲁棒性。

In order to make full use of redundant data of multiple sensors to achieve high-precision tracking,an redundant data adaptive federated Kalman filter algorithm with outlier detection is proposed based on redundant measurement data.First,in the information distribution stage,an adaptive information sharing factor is designed for redundant information,which improves the information distribution efficiency.Secondly,in the information fusion stage,in order to reduce the influence of error data on tracking results,an outlier detection algorithm is proposed,which combines the judgment results of all filters through D-S evidence theory to evaluate whether the data is outlier data.Finally,the linear least square method is used to fuse and obtain a more accurate final estimation result.The simulation results show the proposed algorithm has better tracking accuracy and robustness than the existing models.