基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计

本文将强跟踪滤波理论与多传感器数据融合技术相结合,提出基于强跟踪滤波器的多传感器状态与参数联合估计新算法;对拥有相同采样率的分布式多传感器单模型非线性动态系统,应用强跟踪滤波器,得到目标状态基于全局信息融合估计结果,并利用计算机仿真结果对算法的有效性进行了验证;这些工作初步解决了Kalman滤波中由于模型的不确定性而造成估计误差值偏大情况下的状态融合估计问题,从而丰富和发展了多源信息融合理论.

Joint State and Parameter Estimation for Multisensor Nonlinear Dynamic Systems on the Basis of Strong Tracking Filter

By combining the strong tracking filtering theory with data fusion estimation technology,a new joint state and parameter estimation algorithm of multisensor based on strong tracking filter is proposed.For the multisensor and single model nonlinear dynamic systems having the same sample rates for every sensor,the fusion estimate on the basis of global information by use of strong tracking filter is established,and the effectiveness of the new algorithm is also illustrated by use of an example.These give a primary solution to the fusion estimation problem having bigger errors produced by Kalman filter because of uncertainties of modeling system.This work enriches and develops the information fusion theory.