摘要
针对传统组合导航滤波算法中GNSS量测噪声方差参数不确定问题,本文基于SINS/GNSS动态差分序列原理,对传统Sage-Husa自适应扩展卡尔曼滤波算法(AEKF)根据残差序列信息估计量测方差阵的方法做改进,利用SINS短期定位高精度特性,并结合平滑有界层故障检测算法对GNSS异常观测信息进行隔离,使得改进后的自适应滤波算法能够在GNSS不同噪声环境下保持较高的定位精度。通过实际跑车实验结果表明,在GNSS工作中低密度异常噪声环境下,本文算法相较于EKF算法和传统的Sage-Husa算法平均定位精度提高了39.9%和7.9%,在高密度异常环境下,整体定位精度提升了64.5%和31.9%。因此本文算法有效提高了组合导航系统对不同量测噪声的抗干扰能力。
Aiming at the problem of uncertainty of GNSS measurement noise variance parameter in the traditional combined navigation filtering algorithm,this paper,based on the principle of SINS/GNSS dynamic differential sequence,improves the traditional Sage-Husa adaptive extended Kalman filtering algorithm(AEKF)method of estimating the measurement variance array based on the information of residual sequences,utilizes the high-precision characteristics of short-term positioning of SINS and combines with the smoothing of bounded layers to isolate the abnormal observation information of GNSS,so that the improved adaptive filtering algorithm can maintain a high level of positioning accuracy under different noise environments of GNSS.The fault detection algorithm isolates the abnormal observation information of GNSS,so that the improved adaptive filtering algorithm can maintain high positioning accuracy under different noise environments of GNSS.The experimental results of the actual sports car show that in the low-density anomaly noise environment in GNSS work,the algorithm in this paper improves the average positioning accuracy by 39.9%and 7.9%compared with the EKF algorithm and the traditional Sage-Husa algorithm,and in the high-density anomaly environment,the overall positioning accuracy is improved by 64.5%and 29.1%.Therefore,the algorithm in this paper effectively improves the anti-interference ability of the combined navigation system against different measurement noises.
作者
蔡庸辉
周凌柯
李胜
安昱兴
Cai Yonghui;Zhou Lingke;Li Sheng;An Yuxing(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《电子测量技术》
北大核心
2024年第19期44-53,共10页
Electronic Measurement Technology
基金
高新工程重大专项(5140501B0203)资助。
关键词
组合导航
动态差分序列
自适应扩展卡尔曼滤波
估计量测方差
平滑有界层故障检测
integrated navigation
dynamic difference sequences
adaptive extended Kalman filter
estimating the measurement variance
smoothed bounded-layer fault detection
