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

CHEN Guangwu; WANG Siqi; SI Yongbo; ZHOU Xin

doi:10.11999/JEIT240426

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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

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

doi: 10.11999/JEIT240426

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)

Received Date: 2024-05-29
Rev Recd Date: 2024-09-09

Available Online: 2024-09-17

Abstract

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

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References(22)

References

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