基于Allan方差模型的GNSS/SINS组合导航自适应UKF算法OA
Adaptive UKF Algorithm for GNSS/SINS Integrated Navigation Based on Allan Variance Model
GNSS/SINS组合导航系统是一种典型的非线性系统,采用非线性滤波方法是提高其滤波性能的有效途径之一,而UKF(Unscented Kalman Filter)算法是GNSS/SINS组合导航系统非线性滤波的一种常用且重要的算法,其要求系统噪声方差及测量噪声方差必须准确已知,否则将会导致滤波精度下降甚至发散.采用相应的自适应滤波方法以实时估计测量噪声方差是解决上述问题的核心.基于陀螺噪声分析中的Allan方差分析法,本文提出了一种组合导航系统的自适应UKF算法(ALAUKF).首先,在给出组合导航系统UKF算法流程的基础上,基于当前时刻测量信息及UKF提供的状态向量预测值,构建了Allan方差分析法的样本序列向量;然后,基于Allan方差分析法模型,提出了一种测量噪声方差估计模型,并构建了遗忘因子模型以提高测量噪声方差的估计精度,进而提出了GNSS/SINS组合导航系统的ALAUKF算法;最后将UKF、ALAUKF算法及经典的基于变分贝叶斯自适应滤波算法(VBAUKF)应用于GNSS/SINS组合导航系统非线性模型并进行了实验验证.实验结果表明,当测量噪声均方差未知时,相对VBAUKF,ALAUKF可以高精度地估计测量噪声均方差的各种变化,并可明显提高导航参数的滤波精度,相对于UKF,ALAUKF可以大幅度提高了组合导航系统的滤波精度.综合而言,相对于VBAUKF算法,ALAUKF算法因为具有较少的动态参数及算法简单的优点,为非线性GNSS/SINS组合导航系统的自适应滤波方法研究提出了一条解决途径,丰富了非线性组合导航系统自适应滤波范畴.
The GNSS/SINS integrated navigation system is a typical nonlinear system,so the non-linear filtering methods is one of the effective ways to improve its filtering performance.UKF(Unscented Kalman Filter)algorithm is a common and important algorithm for nonlinear filtering of GNSS/SINS integrated navigation system,which requires that the system noise and measurement noise must be accurately known,otherwise the filtering accuracy will be reduced or even diverged.The appropriate adaptive filtering method to estimate the measurement noise variance in real time is the core of solving the aforementioned problem.An adaptive UKF algorithm(ALAUKF)for integrated navigation system is proposed based on Allan variance analysis in gyro noise modeling.Firstly,based on the algorithm flow of the UKF(Unscented Kalman Filter)for the integrated navigation system,and using the current measurement information and the state vector prediction values provided by the UKF,a sample sequence vector for the Allan variance analysis method was constructed.Then,based on the Allan variance analysis model,a measurement noise variance estimation model was proposed,and an forgetting factor model was constructed to improve the estimation accuracy of measurement noise variance.Subsequently,the ALAUKF algorithm for the GNSS/SINS integrated navigation system was proposed.Finally,the UKF,ALAUKF algorithms and the advanced variational Bayesian adap-tive UKF(VBAUKF)algorithm were applied to the nonlinear model of the GNSS/SINS integrated navi-gation system for experimental verification.The experimental results show that,when the measure-ment noise variance is unknown,compared with VBAUKF,ALAUKF can more accurately estimate various changes in the measurement noise variance(Fig.3 and Fig.4)and significantly improve the filtering accuracy of navigation parameters(Tab.1);and compared with UKF,ALAUKF can greatly enhance the filtering accuracy of the integrated navigation system(Tab.1 and Fig.2).Overall,com-pared with the VBAUKF algorithm,the ALAUKF algorithm has the advantage of a simpler algorithm,because it has fewer dynamic parameters.This has provided a solution approach for the research on adaptive filtering methods for nonlinear GNSS/SINS integrated navigation systems,and has enriched the category of adaptive filtering for nonlinear integrated navigation systems.
刘思明;林雪原;乔玉新
烟台南山学院 智能科学与工程学院,山东 烟台 265713山东外事职业大学,山东 威海 264500烟台南山学院 智能科学与工程学院,山东 烟台 265713
信息技术与安全科学
组合导航Allan方差分析法测量噪声均方差估计自适应滤波
integrated navigationAllan variance analysis methodmeasurement noise MSE esti-mationadaptive filtering
《航空兵器》 2026 (2)
74-80,7
国家自然科学基金项目(62171402)山东省自然科学基金项目(2016ZRA06068)
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