基于自適應(yīng)交互式多卡爾曼濾波模型的組合導(dǎo)航算法研究

陳光武; 王思琪; 司涌波; 周鑫

doi:10.11999/JEIT240426

基于自適應(yīng)交互式多卡爾曼濾波模型的組合導(dǎo)航算法研究

doi: 10.11999/JEIT240426 cstr: 32379.14.JEIT240426

1.
蘭州交通大學(xué)自動化與電氣工程學(xué)院蘭州 730070
2.
甘肅省高原交通信息工程及控制重點實驗室蘭州 730070

基金項目: 甘肅省科技重大專項(21ZD4WA018)，國鐵集團(tuán)科技計劃(N2023G064, N2022G010)，甘肅省自然科學(xué)基金(23JRRA869)，甘肅省科技引導(dǎo)計劃(2020-61-14)

詳細(xì)信息

作者簡介:
陳光武：男，教授，研究方向為智能交通和電子信息

王思琪：女，碩士生，研究方向為慣性導(dǎo)航和組合導(dǎo)航

司涌波：男，工程師，研究方向為慣性導(dǎo)航和組合導(dǎo)航

周鑫：男，博士生，研究方向為慣性導(dǎo)航和組合導(dǎo)航

通訊作者:
王思琪　1491745692@qq.com

中圖分類號: TN713; TP391.9; U495
計量
- 文章訪問數(shù): 367
- HTML全文瀏覽量: 260
- PDF下載量: 56
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-05-29
- 修回日期: 2024-09-09
- 網(wǎng)絡(luò)出版日期: 2024-09-17
- 刊出日期: 2024-12-01

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: 在組合導(dǎo)航系統(tǒng)中，信息融合和定位精度取決于慣性系統(tǒng)和傳感器的特性，然而在實際應(yīng)用中獲取先驗知識仍然具有挑戰(zhàn)性。為解決車輛導(dǎo)航中衛(wèi)星信號質(zhì)量的變化及系統(tǒng)非線性降低組合導(dǎo)航系統(tǒng)性能的問題，該文提出一種基于多卡爾曼濾波器的模糊自適應(yīng)交互式多模型算法(FAIMM-MKF)，將基于衛(wèi)星信號質(zhì)量的模糊控制器(Fuzzy Controller)與自適應(yīng)交互多模型(AIMM)相結(jié)合，通過組合無跡卡爾曼濾波(UKF)、迭代擴(kuò)展卡爾曼濾波(IEKF)和平方根容積卡爾曼濾波(SRCKF)3種不同的濾波器，適配車輛動力學(xué)模型，并通過車載半實物仿真實驗驗證該方法的性能。結(jié)果表明，在衛(wèi)星信號質(zhì)量發(fā)生改變的情況下，與傳統(tǒng)的交互式多模型算法相比，該方法顯著提高了車輛在復(fù)雜環(huán)境中的定位精度。
- 組合導(dǎo)航 /
- 交互式多模型 /
- 卡爾曼濾波器 /
- 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

圖 1 模糊推理系統(tǒng)

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圖 2 隸屬度函數(shù)

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圖 3 FAIMM-MKF算法流程圖

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圖 4 GNSS/SINS/ODO組合導(dǎo)航系統(tǒng)結(jié)構(gòu)

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圖 5 測試路線和實驗設(shè)備示意圖

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圖 6 東向速度和北向速度誤差對比圖

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圖 7 位置誤差對比圖

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圖 8 誤差概率密度圖

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表 1 模糊規(guī)則表

HDOP	模型權(quán)重調(diào)整因子
HDOP	衛(wèi)星拒止	衛(wèi)星較差	衛(wèi)星良好
SP	SP	SP	EP
MP	SP	EP	SP
LP	EP	SP	SP

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表 2 傳感器誤差參數(shù)

性能指標(biāo)	陀螺儀		加速度計
性能指標(biāo)	零偏	隨機(jī)游走	零偏	隨機(jī)游走	更新頻率
參數(shù)	5°/h	0.15°/$ \sqrt h $	0.2 mg	800 ug/$ \sqrt {{\mathrm{Hz}}} $	125 Hz

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表 3 最大誤差和標(biāo)準(zhǔn)誤差對比

算法	東向速度(m/s)		北向速度(m/s)		緯度誤差(m)		經(jīng)度誤差(m)
算法	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)差	最大誤差	標(biāo)準(zhǔn)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 平均絕對誤差和均方根誤差對比

算法	東向速度(m/s)		北向速度(m/s)		緯度(m)		經(jīng)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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