基于自适应抗噪卡尔曼滤波的组合导航方法  

Integrated navigation method based on adaptive anti-noise Kalman filter

作　　者：张溢 顾晶 Zhang Yi;Gu Jing(College of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;College of Electronic Information Engineering,Wuxi University,Wuxi 214105,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]无锡学院电子信息工程学院,无锡214105

出　　处：《电子测量技术》2025年第2期92-100,共9页Electronic Measurement Technology

基　　金：江苏省高等学校基础科学(自然科学)研究面上项目(22KJB140015,23KJB510035)资助。

摘　　要：随着自动驾驶的迅速发展,对高精度车辆导航实时定位技术的需求日益迫切。在常用的GNSS/INS组合导航中,自适应卡尔曼滤波是一种常用的状态预测方法,然而,在复杂的动态环境下,其在应对GNSS多路径噪声和实时变化的过程噪声方面存在局限。针对这一问题,本文提出了一种自适应抗噪卡尔曼滤波算法,用于抑制GNSS测量噪声和动态过程噪声。该算法通过变分模态分解-小波去噪对原始GNSS测量数据进行预处理,提高了数据融合的输入精度;其次,在数据融合过程中,加入了随车辆环境实时变化的动态噪声缩放因子。通过以上两个去噪步骤,整体上有效抑制了噪声不确定性对导航精度的干扰。通过仿真模拟和真实车载实验验证了所提方法的有效性,与传统自适应卡尔曼滤波算法相比,本算法的位置估计和速度估计误差分别降低了37.7%和42.8%,显著提升了移动车辆速度和位置的高精度估计能力。With the rapid development of autonomous driving,the demand for high-precision real-time vehicle navigation and positioning technology is becoming increasingly urgent.In the commonly used GNSS/INS integrated navigation,adaptive Kalman filtering is a standard state prediction method.However,in complex dynamic environments,it has limitations in dealing with multipath noise from GNSS and real-time variations in process noise.To address this issue,this paper proposes an adaptive anti-noise Kalman filtering algorithm to suppress measurement noise from GNSS and dynamic process noise.The algorithm first preprocesses the original GNSS measurement data using variational mode decomposition and wavelet denoising to improve the input accuracy for data fusion.Secondly,during the data fusion process,a dynamic noise scaling factor that changes in real time with the vehicle environment is introduced.Through these two denoising steps,the overall interference of noise uncertainty on navigation accuracy is effectively suppressed.The effectiveness of the proposed method is verified through simulations and real vehicle experiments.Compared with the traditional adaptive Kalman filtering algorithm,the proposed algorithm reduces the position estimation error and speed estimation error by 37.7%and 42.8%,respectively,significantly enhancing the high-precision estimation capability of vehicle speed and position.

关 键 词：组合导航 自适应卡尔曼滤波 抗噪 传感器融合 变分模态分解 

分 类 号：TN966[电子电信—信号与信息处理]