Abstract: In order to improve tracking estimation accuracy of Existing Iterative Extended Kalman Filter（EIEKF），a new method of improved extended kalman filter is proposed.In this paper，iterative filtering theory is introduced into the extended Kalman filter method，and observation information is reused.Taking the classic non-linear and non-gaussian model for example，several simulation experiments are given by using of the method such as Extended Kalman Filter（EKF），Unscented Kalman Filter（UKF），the Existing Iterative Extended Kalman Filter（EIEKF） and new Iterative Extended Kalman Filter.In comparison
with the tracking performance and root mean square error，the new method of Improving Extended Kalman Filter
（NIEKF）method has a higher estimation accuracy.

摘要： 针对现有的迭代扩展卡尔曼滤波（EIEKF）跟踪时估计精度较低这一不足，提出了一种改进扩展卡尔曼滤波（NIEKF）新方法。本文将迭代滤波理论引入到扩展卡尔曼滤波方法中，重复利用观测信息，采用经典的非线性非高斯模型进行仿真实验，给出了该方法与扩展卡尔曼滤波（EKF）、Unscented 卡尔曼滤波（UKF）、现有的迭代扩展卡尔曼滤波（EIEKF）的仿真结果，并分析了其跟踪性能和均方根误差。实验结果表明，改进扩展卡尔曼滤波（NIEKF）新方法具有更高的估计精度。