摘要
本文提出了一种用于非线性系统的多传感器分布式推广卡尔曼滤波算法,该算法中系统的动态方程和传感器的观测方程分别围绕全局估计和全局预测线性化,融合中心基于所有传感器观测的全局估计由各传感器基于自身观测的局部估计来重构。算法分析说明,全局估计的精度高、误差小。最后介绍了文中算法在雷达和红外两种传感器跟踪机动目标中的应用,仿真结果验证了该算法的有效性。
A multisensor distributed extended Kalman filtering algorithm is presented for nonlinear systems, in which the dynamic equations of the systems and the equations of sensor's measurements are linearized in the global estimates and global predictions respectively, and the suboptimal global estimates based on all available information can be reconstructed from the estimates computed by local sensors based solely on their own local information and transmitted to the data fusion center. An analysis of the properties of the algorithm presented here shows that the global estimate has higher precision than the local one and smaller linearization error than the existing method. Finally, an application of the algorithm to radar/IR tracking of a maneuvering target is illustrated. Simulation results show the effectiveness of the algorithm.
基金
国防预研基金
关键词
卡尔曼滤波
目标跟踪
数据融合
雷达
传感器
Extended Kalman filtering
Target tracking
Data fusion
Distributed estimation
