一种基于IMM-SCKF的组合导航算法
Integrated Navigation Algorithm Based on Interactive Multi-Model Square-Root Cubature Kalman Filter
梅方玉 1仇海涛 1王天宇 1张峰2
作者信息
- 1. 北京信息科技大学 高动态导航技术北京市重点实验室,北京 100192
- 2. 北京航天时代光电科技有限公司,北京 100094
- 折叠
摘要
针对在实际应用中组合导航系统存在的噪声干扰多变造成系统滤波精度降低问题,提出了基于交互式多模型(IMM)和平方根容积卡尔曼滤波(SCKF)(IMM-SCKF)算法.IMM-SCKF滤波算法拥有多个模型集,通过调节子模型的概率后进行融合输出,能够尽可能地模拟实际噪声协方差.仿真试验和道路试验结果均表明,IMM-SCKF算法的速度误差和位置误差均方根均优于传统单模型CKF算法,能有效提高组合导航系统的可靠性.在实际道路跑车试验中,与传统CKF算法相比,IMM-SCKF算法的东、北、天速度误差均方根分别降低了52%、55%、30%,位置误差均方根分别降低了47%、60%、32%,IMM-SCKF算法显著提高了系统的定位精度及抗干扰能力.
Abstract
To address the issue of reduced filtering accuracy in integrated navigation systems caused by variable noise interference,an algorithm based on interactive multi-model(IMM)and square-root cubature Kalman filter(SCKF)is proposed.The IMM-SCKF filtering algorithm employs multiple model sets and adjusts the probability of the sub-model while fusing the output,allowing it to simulate the actual noise covariance to a certain degree.Simu-lation and road test results show that the root mean square(RMS)error of the IMM-SCKF algorithm is superior to that of the traditional single-model CKF algorithm,effectively enhancing the reliability of the integrated navigation system.Compared to the traditional CKF algorithm,the IMM-SCKF algorithm reduced the RMS error in eastward,northward,and up speed errors by 52%,55%,and 30%,respectively,and the RMS error in position by 47%,60%,and 32%,respectively.The IMM-SCKF algorithm significantly improves the positioning accuracy and anti-interference ability of the system.
关键词
组合导航/交互式多模型/平方根容积卡尔曼滤波/融合输出/抗干扰能力Key words
integrated navigation/interactive multi-model/square-root cubature Kalman filter/fusion output/anti-interference capability引用本文复制引用
基金项目
国家自然科学基金资助项目(61703040)
出版年
2024
NETL
NSTL
万方数据