GNSS/SINS 组合导航系统的非线性最大熵 UKF 算法

林雪原, 孙炜玮

林雪原, 孙炜玮. GNSS/SINS 组合导航系统的非线性最大熵 UKF 算法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240045
引用本文: 林雪原, 孙炜玮. GNSS/SINS 组合导航系统的非线性最大熵 UKF 算法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240045
LIN Xueyuan, SUN Weiwei. Nonlinear Maximum Correntropy UKF Algorithm for GNSS/SINS Integrated Navigation System[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240045
Citation: LIN Xueyuan, SUN Weiwei. Nonlinear Maximum Correntropy UKF Algorithm for GNSS/SINS Integrated Navigation System[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240045

GNSS/SINS 组合导航系统的非线性最大熵 UKF 算法

基金项目: 

国家自然科学基金(62076249)

山东省自然科学基金(ZR2020MF154) 。

详细信息
    作者简介:

    林雪原,博士,教授,主要从事组合导航及其信息融合的研究。linxy_ytcn@126.com

    通讯作者:

    孙炜玮,硕士,副教授。s353375092@qq.com

Nonlinear Maximum Correntropy UKF Algorithm for GNSS/SINS Integrated Navigation System

  • 摘要: 无迹卡尔曼滤波器(UKF) 是解决 GNSS/SINS 组合导航系统非线性滤波的一种有效方法, UKF 在高斯噪声中能表现出良好的性能,但在非高斯噪声中、尤其是重尾非高斯噪声环境下,其性能会严重下降。为了提高UKF 对重尾非高斯噪声的鲁棒性,本文提出了一种 GNSS/SINS 组合导航系统的最大熵 UKF(MCUKF)算法。首先,建立了 GNSS/SINS 组合导航系统的非线性系统模型,其特点为状态方程为非线性而测量方程为线性;然后,利用 UKF 的无迹变换(UT),获得了状态及其协方差矩阵的先验估计;最后,利用最大熵准则和统计线性回归模型获得状态及其协方差矩阵的后验估计,并设计了 MCUKF 算法的不动点迭代实现步骤。仿真实验表明,在高斯噪声中 UKF 性能略优于 MCUKF;而在重尾噪声环境下,相对于 UKF,核带宽为 5 的 MCUKF 可提高位置精度 13.4%、提高速度精度 10.3%;相对于 AKF, MCUKF 可提高位置精度 8.8%、提高速度精度 7.5%。实验结果表明,在较小的核带宽时, MCUKF 的滤波性能明显优于 UKF,可提升复杂环境下组合导航系统的滤波精度。
    Abstract: Objectives: Unscented Kalman filter (UKF) is an effective method to solve the nonlinear filtering of GNSS/SINS integrated navigation system. UKF shows good performance in Gaussian noise, but its performance will be seriously degraded in non-Gaussian noise, especially when the system is interfered by some heavy tail pulse noise. In order to improve the robustness of UKF against heavy tail pulse noise, this paper proposed a maximum correntropy UKF (MCUKF) algorithm for GNSS/SINS integrated navigation system is proposed in this paper. Methods: Firstly, the nonlinear system model of GNSS/SINS integrated navigation system is established, which is characterized by nonlinear state equation and linear measurement variance. Then, a prior estimation of the state and its covariance matrix is obtained by using the unscented transform (UT) of UKF. Finally, the maximum correntropy criterion and statistical linear regression model are used to obtain the posterior estimation of the state and its covariance matrix, and the fixed point iteration of MCUKF algorithm is designed. Results: The simulation results show that the performance of UKF is slightly better than MCUKF in Gaussian noise; Compared with UKF, MCUKF with a core bandwidth of 5 can improve position accuracy by 13.4% and velocity accuracy by 10.3%; Compared with AKF, MCUKF can improve position accuracy by 8.8% and speed accuracy by 7.5%. Conclusions: The experimental results show that the filtering performance of MCUKF is obviously better than that of UKF at small core bandwidth, which can improve the filtering accuracy of integrated navigation system in complex environment.
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  • 收稿日期:  2024-09-14

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