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Localization Method for Underwater Magnetic Sensors in the Fixed Deperming Station |
Wang Yufen, Zhou Guohua, Wu Kena, Li Linfeng |
College of Electrical Engineering Naval University of Engineering Wuhan 430033 China |
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Abstract At present, the main way to measure a ship's magnetic field is by the magnetic sensor array installed on the submarine plane of the fixed deperming station. The installation of underwater magnetic sensors is particularly difficult due to the complex and changeable submarine environment. There is a certain position deviation between the installation position of the underwater magnetic sensors and the preset position in actual engineering, which is the main factor affecting the magnetic field measurement of the ship. In addition, with the continuous development of magnetic mines and airborne magnetic prospecting, the requirements for the magnetic protection capability of ships are becoming increasingly strict. Therefore, combining magnetic field and depth measurement data, a localization method for underwater magnetic sensors is proposed based on optimizing the magnetic field gradient has been proposed. Firstly, the pier meshes with a suitable grid spacing l. Secondly, the energized solenoid coil is equivalent to a magnetic dipole. When the magnetic dipole is located at each grid node, the average magnetic field gradients in the x-axis and y-axis directions of the region 60 cm×60 cm×60 cm centered on the sensor are calculated. Simultaneously, the coil coordinates are recorded. Then the average magnetic field gradients in the three directions are sorted from large to small. Thirdly, the magnetic source is moved on the grid nodes corresponding to the first k average magnetic field gradients in the x-axis and y-axis directions, and underwater magnetic sensors measure the magnetic field of the magnetic source. Fourthly, the magnetic source data are measured in the x-axis and y-axis directions, and the depth data are used to construct the objective function. A novel multi-swarm particle swarm optimization with a dynamic learning strategy is used to obtain the x-axis and y-axis components of the position deviation. In the locating model, the moving positions of the magnetic source are determined by optimizing the average magnetic field gradients in the x-axis and y-axis directions. Hence, the localization accuracy of the magnetic sensors in the x-axis and y-axis directions is greatly improved. In addition, the depth of the magnetic sensor is measured by the depth sensor, which avoids the imprecise solution of z-axis components. In this paper, the effectiveness of this method is proved by numerical simulation experiments and physical scale model experiments under the size of typical degaussing stations. The following main conclusions are drawn as follows: (1) The relationship between the position correction error and the number of moving positions k of the magnetic source is studied quantitatively. When the number of moving positions k of the magnetic source reaches 12 times under the grid spacing of 2 m, the position correction error no longer decreases with the increase of k and basically reaches the saturation state. (2) Through numerical simulations, the influence of environmental magnetic noise on the localization of underwater magnetic sensors is the most significant. The numerical localization experiment of underwater magnetic sensors in different position deviation states was carried out. After correction, the mean value of the average error of sensors was 0.023 m, the mean value of the maximum error was 0.056 m, and the mean value of the variance was 2.26×10-4 m2, which met the requirement for magnetic field measurement of ships in a degaussing station. (3) The physical scale model experiment of the fixed deperming station is designed. The experimental results show that the average error of the sensor is 5 mm, and its amplification is 0.043 m according to the scale (1 8.5), almost consistent with the numerical simulation results. The effectiveness and accuracy of the proposed method are verified.
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Received: 03 November 2022
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