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

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

1.
兰州交通大学自动化与电气工程学院兰州 730070
2.
甘肃省高原交通信息工程及控制重点实验室兰州 730070

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
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- 文章访问数: 82
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- 被引次数: 0
出版历程
- 收稿日期: 2024-05-29
- 修回日期: 2024-09-09
- 网络出版日期: 2024-09-17

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

1.
School of Automation and Electeical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China

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

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。
- 组合导航 /
- 交互式多模型 /
- 卡尔曼滤波器 /
- 模糊控制器
Abstract: Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
- Intergrated navigation /
- Interacting Multiple Model(IMM) /
- Kalman Filter(KF) /
- Fuzzy controller

HTML全文

图 1 模糊推理系统

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图 2 隶属度函数

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图 3 FAIMM-MKF算法流程图

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图 4 GNSS/SINS/ODO组合导航系统结构

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图 5 测试路线和实验设备示意图

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图 6 东向和北向速度误差对比图

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图 7 位置误差对比图

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图 8 误差概率密度图

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表 1 模糊规则表

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

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表 2 传感器误差参数

	陀螺仪		加速度计
性能指标	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

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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)
算法	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

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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)
算法	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
IMM-UKF	0.030 2	0.055 0	0.038 1	0.059 6	0.309 3	0.397 1	0.568 3	0.873 6
IMM-SRCKF	0.024 9	0.043 0	0.027 9	0.048 8	0.235 2	0.311 7	0.481 5	0.743 3
IMM-MKF	0.024 6	0.039 9	0.026 1	0.047 6	0.190 4	0.303 4	0.440 1	0.729 5
FAIMM-MKF	0.018 3	0.029 1	0.025 6	0.043 6	0.164 5	0.226 9	0.364 0	0.642 0

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