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自1960年卡尔曼滤波器问世以来,它在众多军事和民用导航系统中已成为不可或缺的组成部分。这一极其简单的递推数字算法在其出现的初期就帮助人们方便地综合(或熔合)导航敏感器的数据以实现系统性能总体优化。为了获得系统变量(如位置)的当前估计值,滤波器使用统计模型,利用已有的信息给每一个新的测量值适当加权。它还可以确定出最新时刻估计值的不确定性,用于实时性能评价和系统的离线设计研究。由于卡尔曼滤波器的最优性能、通用性且易于实现,它在GPS组合惯导系统和单独使用的GPS装置中得到尤为普遍的关注。本文概述卡尔曼滤波器及其在GPS导航中的应用。
Since the advent of the Kalman filter in 1960, it has become an integral part of many military and civil navigation systems. This extremely simple recursive digital algorithm helped to easily integrate (or fuse) navigation sensor data for overall system performance optimization at the beginning of its existence. In order to obtain the current estimate of the system variables (such as position), the filter uses a statistical model, using the existing information to appropriately weight each new measurement. It can also determine the uncertainty of the latest time estimates for real-time performance evaluation and offline design of the system. Due to its optimal performance, versatility and ease of implementation, the Kalman filter is of particular interest in GPS combined inertial navigation systems and in stand-alone GPS devices. This article outlines the Kalman filter and its application in GPS navigation.