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基于自适应交互式多卡尔曼滤波模型的组合导航算法研究

陈光武 王思琪 司涌波 周鑫

陈光武, 王思琪, 司涌波, 周鑫. 基于自适应交互式多卡尔曼滤波模型的组合导航算法研究[J]. 电子与信息学报. doi: 10.11999/JEIT240426
引用本文: 陈光武, 王思琪, 司涌波, 周鑫. 基于自适应交互式多卡尔曼滤波模型的组合导航算法研究[J]. 电子与信息学报. doi: 10.11999/JEIT240426
CHEN Guangwu, WANG Siqi, SI Yongbo, ZHOU Xin. Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240426
Citation: CHEN Guangwu, WANG Siqi, SI Yongbo, ZHOU Xin. Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240426

基于自适应交互式多卡尔曼滤波模型的组合导航算法研究

doi: 10.11999/JEIT240426
基金项目: 甘肃省科技重大专项(21ZD4WA018),国铁集团科技计划(N2023G064, N2022G010), 甘肃省自然科学基金(23JRRA869),甘肃省科技引导计划(2020-61-14)
详细信息
    作者简介:

    陈光武:男,教授,研究方向为智能交通和电子信息

    王思琪:女,硕士生,研究方向为惯性导航和组合导航

    司涌波:男,工程师,研究方向为惯性导航和组合导航

    周鑫:男,博士生,研究方向为惯性导航和组合导航

    通讯作者:

    王思琪 1491745692@qq.com

  • 中图分类号: TN713; TP391.9; U495

Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling

Funds: Gansu Province Science and Technology Major Project (21ZD4WA018), The National Railway Group Science and Technology Program Project (N2023G064, N2022G010), Gansu Province Natural Science Foundation (23JRRA869), Gansu Province Science and Technology Guidance Program (2020-61-14)
  • 摘要: 在组合导航系统中,信息融合和定位精度取决于惯性系统和传感器的特性,然而在实际应用中获取先验知识仍然具有挑战性。为解决车辆导航中卫星信号质量的变化及系统非线性降低组合导航系统性能的问题,该文提出一种基于多卡尔曼滤波器的模糊自适应交互式多模型算法(FAIMM-MKF),将基于卫星信号质量的模糊控制器(Fuzzy Controller)与自适应交互多模型(AIMM)相结合,通过组合无迹卡尔曼滤波(UKF)、迭代扩展卡尔曼滤波(IEKF)和平方根容积卡尔曼滤波(SRCKF)3种不同的滤波器,适配车辆动力学模型,并通过车载半实物仿真实验验证该方法的性能。结果表明,在卫星信号质量发生改变的情况下,与传统的交互式多模型算法相比,该方法显著提高了车辆在复杂环境中的定位精度。
  • 图  1  模糊推理系统

    图  2  隶属度函数

    图  3  FAIMM-MKF算法流程图

    图  4  GNSS/SINS/ODO组合导航系统结构

    图  5  测试路线和实验设备示意图

    图  6  东向和北向速度误差对比图

    图  7  位置误差对比图

    图  8  误差概率密度图

    表  1  模糊规则表

    HDOP 模型权重调整因子
    卫星拒止 卫星较差 卫星良好
    SP SP SP EP
    MP SP EP SP
    LP EP SP SP
    下载: 导出CSV

    表  2  传感器误差参数

    性能指标 陀螺仪 加速度计
    零偏 随机游走 零偏 随机游走 更新频率
    参数 5°/h 0.15°/$ \sqrt h $ 0.2 mg 800 ug/$ \sqrt {{\mathrm{Hz}}} $ 125 Hz
    下载: 导出CSV

    表  3  最大误差和标准误差对比

    算法 东向速度(m/s) 北向速度(m/s) 纬度误差(m) 经度误差(m)
    最大误差 标准差 最大误差 标准差 最大误差 标准差 最大误差 标准差
    IMM-UKF 1.080 0 0.048 5 0.347 1 0.055 2 1.151 5 0.395 6 –2.919 1 0.835 6
    IMM-SRCKF 0.623 7 0.042 4 0.288 3 0.045 1 0.974 2 0.311 6 –2.439 4 0.705 3
    IMM-MKF 0.483 5 0.038 6 0.280 6 0.043 8 0.941 0 0.291 5 –2.193 6 0.678 7
    FAIMM-MKF 0.467 1 0.028 4 0.205 3 0.040 2 0.760 8 0.220 1 –1.962 5 0.589 3
    下载: 导出CSV

    表  4  平均绝对误差和均方根误差对比

    算法东向速度(m/s)北向速度(m/s)纬度(m)经度(m)
    MAERMSEMAERMSEMAERMSEMAERMSE
    IMM-UKF0.030 20.055 00.038 10.059 60.309 30.397 10.568 30.873 6
    IMM-SRCKF0.024 90.043 00.027 90.048 80.235 20.311 70.481 50.743 3
    IMM-MKF0.024 60.039 90.026 10.047 60.190 40.303 40.440 10.729 5
    FAIMM-MKF0.018 30.029 10.025 60.043 60.164 50.226 90.364 00.642 0
    下载: 导出CSV
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  • 收稿日期:  2024-05-29
  • 修回日期:  2024-09-09
  • 网络出版日期:  2024-09-17

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