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• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

The deep-sea pose measurement system can measure the relative distance and angle of two subsea pipelines which the connection of submarine pipelines project need, and the success of connection is mostly decided by high measurement accuracy. According to deep-sea environment, paper designs a measuring method by deep-sea pipeline pose measurement system which bases on the stretching wire. In order to increasing measurement accuracy, paper analyses the transitive relation of error source which impact system measurement accuracy, and gets the impact laws of error sources which include the orthogonal angle, extension arm vertical pitching angle and horizontal swing angle, etc. We revise the measuring result by binary linear regression analysis to improve the measurement accuracy. Through constructing the test platform, we compare measuring value and true value of the pipelines pose parameters. The experimental results show that the distance error is ±30 mm inside, and that the angle error is ±0.7° inside within 7 m after correction. We reduce the measurement error of the pose measurement system, and verify the correctness of the theoretical. [ABSTRACT FROM AUTHOR]

• A unified error model of the geomagnetic measurement data was established. • A two-stage Kalman filter method was proposed to perform data fusion. • An adaptive attitude updating algorithm is proposed to eliminate data interference. • The high-precision geomagnetic directional technology was achieved. Low-cost magnetic and inertial sensors based on micro-electro-mechanical systems (MEMS) are extensively used for navigation systems. However, measurements of the magnetometer are very susceptible to different factors, resulting in low accuracy when used for attitude estimation. To solve these problems, a novel method is proposed for geomagnetic directional technology based on sensor error correction and adaptive hybrid filter. In the dynamic directional accuracy test with the turntable rotating speed of 40°/s, the maximum error and RMSE of the yaw angle is 1.00° and 0.63°, respectively. When magnetic interference exists, this algorithm dynamically adjusts the weighting of the magnetometer data by information entropy and RMSE. The experimental results show that the adaptive attitude updating algorithm can reduce the directional error by 90.61% in the environment of magnetic interference. It can meet the overall demand for high-precision attitude calculation of different carriers. [ABSTRACT FROM AUTHOR]