基于自适应卡尔曼滤波算法的紧组合导航系统的研究
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TN967.2

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Research on tight integrated navigation system based on adaptive Kalman filter algorithm
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    摘要:

    为了提高子滤波器滤波精度和优化信息融合算法,提出一种基于在线调节因子的自适应卡尔曼滤波算法。首先讨论采用卡尔曼滤波技术的理论依据,设计SINS/GPS紧组合导航系统。提出改进的自适应卡尔曼滤波算法,该方法通过构造自适应参数因子,并利用量测噪声协方差阵与自适应参数的比值实现在线修正量测噪声协方差阵。通过MATLAB仿真,与传统基于标准卡尔曼滤波算法的紧组合导航系统相比,其各向位置误差和速度误差均得到明显降低,从而达到提高组合导航定位精度和优化信息融合算法的目的。

    Abstract:

    In order to improve the sub-filter filtering accuracy and optimize the information fusion algorithm, an adaptive Kalman filtering algorithm based on online adjustment factor is proposed. First of all, discuss the theoretical basis of using Kalman filter technology, and design SINS/GPS tight integrated navigation system.An improved adaptive Kalman filter algorithm is proposed. By constructing an adaptive parameter factor and using the ratio of the measured noise covariance matrix to the adaptive parameters, the online correction measurement noise covariance matrix is realized. Through the result of MATLAB simulation, its position error and speed error are significantly reduced, compared with traditional tightly integrated navigation systems based on standard Kalman filter algorithm, so as to improve the positioning accuracy of the integrated navigation system and optimizing information fusion algorithm.

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刘军,刘克诚,田甜,崔学伟.基于自适应卡尔曼滤波算法的紧组合导航系统的研究[J].电子测量技术,2019,42(5):52-55

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  • 在线发布日期: 2021-07-29
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