College of Electrical and Power Engineering, Taiyuan University of Technology

【目的】在导航定位系统中,基于卡尔曼滤波框架的多径误差抑制算法是提高定位精度的有效方法。但是,在算法的过程噪声和观测噪声协方差初值的选取不当时,会导致估计结果误差很大甚至发散。另外,由于此类算法是基于最小均方误差准则,算法在受到非高斯噪声干扰时尤其是重尾非高斯噪声,会出现估计精度显著下降的问题。【方法】为了在高斯噪声和非高斯噪声下都能够保持较好的多径估计结果提高定位精度,本文提出一种自适应最大相关熵无迹卡尔曼滤波(adaptivemaximumcorrentropyunscentedkalmanfilter,AMCUKF)多径估计算法,算法在观测更新过程中引入最大相关熵作为优化准则,以解决在非高斯噪声下的估计精度下降的问题,在噪声协方差更新过程中用观测量的残差序列对噪声协方差矩阵进行递归更新,取代过程噪声和观测噪声协方差初值的选取。【结果】在高斯噪声和非高斯噪声下分别进行了仿真实验,通过与两种基于卡尔曼滤波框架的估计算法进行对比表明,AMCUKF多径算法不仅能够在高斯噪声下保持较好的多径估计结果,而且在非高斯噪声下也能够保持更高的多径估计精度,有效抑制非高斯噪声的干扰。

【Purposes】 In navigation and positioning system, the multipath estimation algo-rithms based on Kalman filter framework can effectively improve the positioning accuracy. When the initial value of the process noise and observation noise covariance of such algorithms is im-properly selected, a large error or even divergence of the estimation results may occur. In addi-tion, because the algorithm is based on the minimum mean squared error criterion, it is suscepti-ble to non-Gaussian noise, especially under heavy-tailed non-Gaussian noise, which has the prob-lem of significant degradation of estimation accuracy. 【Methods】 In order to maintain good mul-tipath estimation results under both Gaussian noise and non-Gaussian noise and improve positio-ning accuracy, an adaptive maximum correntropy unscented Kalman Filter (AMCUKF) mul-tipath estimation algorithm is proposed in this paper. The AMCUKF algorithm introduces the maximum correntropy as an optimization criterion in the process of observation update to solve the problem of estimation accuracy degradation under non-Gaussian noise. In the process of noise covariance update, the residual sequence of the observed quantity is used to recursively update the noise covariance to solve the improper initial value selection of the process noise and the observed noise covariance. 【Findings】 Simulation experiments are carried out under Gaussian noise and non-Gaussian noise, and by comparing with two estimation algorithms based on Kalman filter framework, it is shown that AMCUKF multipath algorithm can not only maintain better mul-tipath estimation results under Gaussian noise, but also maintain higher multipath estimation ac-curacy under non-Gaussian noise, effectively suppressing the interference of non-Gaussian noise.

国家自然科学基金项目资助(61603267);基于域适应迁移的未知模态下磨矿粒度分布在线软测量和控制方法研究(61973226)

张晋恒,程兰,张净,等.基于改进无迹卡尔曼滤波的多径估计算法[J].太原理工大学学报,2023,54(5):877-884.

ZHANG Jinheng,CHENG Lan,ZHANG Jing,et al.Multipath estimation algorithm based on improved unscent-ed Kalman filter[J].Journal of Taiyuan University of Technology,2023,54(5):877-884.