Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

Zhao, Huijun; Niu, Xiaoming; Cao, Huiliang; Shen, Chong; Wang, Chenguang; Li, Jie; Tang, Jun; Liu, Jun

doi:10.1109/tim.2023.3262253

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Currently, global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) are the most typical and widely used navigation methods in the field of the Internet of Things (IoT), such as the Internet of Vehicles (IoV) [1][2][3][4]. However, as a result of the rapid developments in bionic technology [5], some new types of bionic autonomous navigation technologies have emerged that are based on biological principles and can help unmanned platforms including unmanned aerial vehicles (UAVs) [6], unmanned vehicles (UVs) [7][8][9], and autonomous underwater vehicles (AUVs) to realize autonomous navigation. In recent years, polarization navigation based on atmospheric polarized light [10] has been noted as a more mature technique for bionic autonomous navigation technology.…”

Section: Introductionmentioning

confidence: 99%

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Zhao,

Shen,

Cao

et al. 2024

Micromachines

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The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

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