摘要
标准的卡尔曼滤波可以扩展到非线性模型，即将泰勒公式应用于状态方程和观测方程，得到扩展卡尔曼滤波公式。首先推导了计算公式，研究了迭代计算方法，并将其用于ＧＰＳ数据的实时处理。
The Kalman filtering is a method with which the raw data with noise can be cleaned. The standard KF can be extended to a non linear model. In the extended Kalman filter (EKF), Taylor proximate formula has been applied to both state equations and measurement equations, in order to estimate linearized dynamical models. But if the initial value is incorrect or the noise is very strong, the linearized models may not be good anymore. The iterated extended Kalman filter (IEKF) therefore has been applied to GPS raw data processing, and the results are satisfactory.
出处
《武汉测绘科技大学学报》
CSCD
1999年第2期112-114,123,共4页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金
