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说明：  运动载体的定位与导航一般应用Kalman滤波算法。可 靠的Kalman滤波算法要求有可靠的函数模型、随机模型以 及合理的估计方法。实际情况中运动载体一般难以确保规 则运动，因而精确的函数模型的构造十分困难。近年来， 有不少学者对机动载体运动模型进行了讨论，所属内容各 具特点，建立的各种模型包括微分多项式模型、常速度模 型、常加速度模型、时间相关模型，半马尔柯夫模型、 Noval统计模型及机动载体的“当前”统计模型(Moving vehicle positioning and navigation general application of the Kalman filter algorithm. Kalman filter algorithm requires a reliable function model, a stochastic model, as well as a reasonable estimation method. Moving carrier in the actual situation is generally difficult to ensure a regular motion, and thus the the precise function model structure is very difficult. In recent years, many scholars have discussed the movement of the motor carrier model, each with distinct characteristics of their respective content, establish various models including the differential polynomial model, constant velocity model, constant acceleration model, time-dependent model, semi-Markov model, Noval statistical model and motor carrier " current" statistical model)

GPS导航中的抗差自适应Kalman滤波算法.pdf,295819,2013-03-29