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A new application of Kalman filtering algorithm based on interval calculation in navigation system

机译:卡尔曼滤波算法在导航系统中间隔计算的新应用

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摘要

In traditional Kalman filtering algorithm, the system noise and observation noise should be assumed as zero-mean Gaussian white noise, meanwhile need the state-space model and relevant references be given and accurate. However, the white noise is just an ideal noise model that doesn't exist in real environment. This paper analyzed the effect to filtering result from the statistical estimation in traditional Kalman filtering algorithm and brought interval calculation into traditional Kalman filtering algorithm, which based on the concept of interval and could improve the robustness of the system, decrease the error caused by the statistical estimation of noise model.
机译:在传统的卡尔曼滤波算法中,系统噪声和观察噪声应假定为零表示高斯白噪声,同时需要提供状态空间模型和相关参考,并准确。然而,白噪声只是真实环境中不存在的理想噪声模型。本文分析了传统卡尔曼滤波算法中统计估算结果的效果,并将区间计算到传统的卡尔曼滤波算法,这基于间隔的概念,可以提高系统的稳健性,降低由统计造成的错误噪声模型的估计。

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