Study on FOG scale factor error calibration in start-up stage based on GWO-GRU

Wang, Fei; Zhao, Shuai; Wang, Lei; Zhou, Yilan; Huang, Tengchao; Shu, Xiaowu

doi:10.1016/j.measurement.2022.112214

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Figure 11 presents the temperature variations observed during these two experiments, spanning approximately 35 • C. Given the significant impact of temperature on the drifts, biases, and scale factor errors of fiber optic gyros and quartz accelerometers [22][23][24][25], such errors in inertial devices have been pre-compensated according to temperature to prevent causing navigation errors. And on this premise, in figure 12, the attitude outputs under three different conditions for each experiment are displayed: (1) without compensation for non-orthogonal angles; (2) without compensation for temperature errors of non-orthogonal angles; (3) temperature compensation applied based on the model described in section 4.…”

Section: Navigation Experimentsmentioning

confidence: 99%

A self-calibration method for temperature errors of non-orthogonal angles between gimbals in rotational inertial navigation system

Liu,

Wang,

2024

Meas. Sci. Technol.

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The Rotational Inertial Navigation System (RINS) employs a rotating modulation technique by introducing rotation gimbals, thereby enhancing RINS' accuracy in position and velocity estimation. Furthermore, the rotation gimbals provide the ability for self-calibration in RINS. However, it introduces new sources of error, such as non-orthogonal angles between gimbals, which result in inaccuracies in attitude outputs and thus require precise calibration and compensation. Previous research employs RINS attitude errors as measurements in the Kalman filter (KF) for calibration. However, attitude measurements are susceptible to various factors, and the influence of temperature on non-orthogonal angles has been largely overlooked before. In this paper, we discuss the causes of temperature-induced errors in non-orthogonal angles between gimbals and their impact on attitude estimation. And a self-calibration method based on outputs from fiber optic gyroscopes and optical angle encoders is proposed. The results of navigation experiments validate the effectiveness of the calibration and compensation method. After compensating the temperature errors of the non-orthogonal angles between gimbals, we can see a decrease in the amplitude of attitude output oscillation from 80" to less than 10".

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Section: Navigation Experimentsmentioning

confidence: 99%

A self-calibration method for temperature errors of non-orthogonal angles between gimbals in rotational inertial navigation system

Liu,

Wang,

2024

Meas. Sci. Technol.

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“…To optimize the Radio Resource Management (RRM), a fractional GWO-CS optimization model integrating the GWO with the cuckoo search (CS) algorithm has been proposed, which optimizes parameters like the power spectral density (PSD), the transmission power, and the sensing bandwidth (SB) [54]. GWO has been combined with a gated recurrent unit (GRU) neural network to compensate for the reduced calibration time and ensure the fibre optic gyroscope's (FOG) calibration accuracy [55]. The GWO algorithm combining the Integer Wavelet Transform (IWT) reduces information loss regarding image steganography [56].…”

Section: • Practical Applicationmentioning

confidence: 99%

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

Tian

Chen

et al. 2023

Mathematics

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Metaheuristic optimization algorithms play a crucial role in optimization problems. However, the traditional identification methods have the following problems: (1) difficulties in nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification rates resulting from premature convergence. This paper proposed a variant based on the gray wolf optimization algorithm (GWO) with chaotic disturbance, candidate migration, and attacking mechanisms, naming it the enhanced gray wolf optimizer (EGWO), to solve the problem of premature convergence and local stagnation. The performance of the EGWO was tested on IEEE CEC 2014 benchmark functions, and the results of the EGWO were compared with the performance of three GWO variants, five traditional and popular algorithms, and six recent algorithms. In addition, EGWO optimized the weights and biases of a multi-layer perceptron (MLP) and proposed an EGWO-MLP disease identification model; the model was tested on IEEE CEC 2014 benchmark functions, and EGWO-MLP was verified by UCI dataset including Tic-Tac-Toe, Heart, XOR, and Balloon datasets. The experimental results demonstrate that the proposed EGWO-MLP model can effectively avoid local optimization problems and premature convergence and provide a quasi-optimal solution for the optimization problem.

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Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

Huang,

Wang,

Dong

et al. 2024

Stoch Environ Res Risk Assess

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Study on FOG scale factor error calibration in start-up stage based on GWO-GRU

Cited by 6 publications

References 19 publications

A self-calibration method for temperature errors of non-orthogonal angles between gimbals in rotational inertial navigation system

A self-calibration method for temperature errors of non-orthogonal angles between gimbals in rotational inertial navigation system

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

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