Aiming at a kind of nonlinear multisensor systems,we study three classic nonlinear centralized fusion algorithms based on the extend Kalman filter (EKF) and extend some fusion theories for linear dynamic systems to nonlinear systems.On the basis of extend information filter (EIF),three kinds of fusion algorithms such as augmented measurements fusion,measurements weighted fusion and sequential filtering fusion are presented.Afterwards,we compare estimate accuracies of the three nonlinear fusion algorithms,and discuss the exchanging property of measurement's update order.The results are as follows.Firstly,when measurement properties are identical,the estimate of the augmented measurements fusion algorithm and the measurements weighted fusion algorithm are equivalcent.Secondly,the estimate accuracy of the sequential filtering fusion does not hold completely functional equivalence in linear systems as the other two fusion methods.Thirdly,the exchanging property of the measurement's update order of nonlinear sequential filtering fusion can no longer be guaranteed.Four examples based on bearings-only tracking are shown to demonstrat the validity of the conclusions.%以一类非线性多传感器动态系统为对象,基于扩展Kalman滤波器(Extend Kalman filter,EKF)介绍三种典型非线性集中式融合算法,并以此为基础研究部分线性动态系统融合理论在非线性系统中的推广与完善.首先,利用EKF的一种信息滤波器形式(Extend information filter,EIF)给出测量值扩维融合、测量值加权融合和顺序滤波融合算法公式,进而研究三种非线性融合算法的估计性能比较以及测量值融合更新次序是否满足可交换性.结果表明:当各传感器的测量特性相同时,集中式测量值扩维和测量值加权融合算法的估计精度功能等价;非线性顺序滤波融合与其他两种融含算法之间不再具备线性多传感器系统中估计功能的完全等价特性;在融合精度不变前提下非线性顺序滤波融合中,各传感器观测更新次序不再完全满足可交换性.4个基于纯方位目标跟踪的数值仿真被用来验证文中所得结论的有效性和正确性.
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