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 (18.5), almost consistent with the numerical simulation results. The effectiveness and accuracy of the proposed method are verified.
Wang Yufen,Zhou Guohua,Wu Kena等. Localization Method for Underwater Magnetic Sensors in the Fixed Deperming Station[J]. Transactions of China Electrotechnical Society, 2024, 39(4): 956-965.
[1] 王锴松, 周国华, 刘月林, 等. 地磁模拟法测量舰船感应磁场的数值模拟[J]. 兵工学报, 2022, 43(3): 617-625. Wang Kaisong, Zhou Guohua, Liu Yuelin, et al.Numerical simulation of measuring ship's induced magnetic field by geomagnetic field simulation method[J]. Acta Armamentarii, 2022, 43(3): 617-625. [2] 曹恒佩, 艾萌萌, 王延波. 永磁辅助同步磁阻电机研究现状及发展趋势[J]. 电工技术学报, 2022, 37(18): 4575-4592. Cao Hengpei, Ai Mengmeng, Wang Yanbo.Research status and development trend of permanent magnet assisted synchronous reluctance motor[J]. Transa- ctions of China Electrotechnical Society, 2022, 37(18): 4575-4592. [3] 刘琪, 孙兆龙, 姜润翔, 等. 一种舰船下方磁场的信号重构及换算方法[J]. 电工技术学报, 2022, 37(15): 3723-3732. Liu Qi, Sun Zhaolong, Jiang Runxiang, et al.A signal reconstruction and conversion method of magnetic field under ship[J]. Transactions of China Electro- technical Society, 2022, 37(15): 3723-3732. [4] Hott M, Harlakin A, Hoeher P A.Hybrid com- munication and localization underwater network nodes based on magnetic induction and visible light for AUV support[C]//2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2020: 66-68. [5] Callmer J, Skoglund M, Gustafsson F.Silent localization of underwater sensors using magneto- meters[J]. Eurasip Journal on Advances in Signal Processing, 2010: 1-8. [6] 张朝阳, 肖昌汉. 海底布放磁传感器的磁定位方法的模拟实验研究[J]. 上海交通大学学报, 2011, 45(6): 826-830. Zhang Zhaoyang, Xiao Changhan.Simulation experi- ment research for magnetic localization method for magnetometer sensor at seabed[J]. Journal of Shanghai Jiao Tong University, 2011, 45(6): 826-830. [7] 杨明明, 刘大明, 连丽婷, 等. 用海面磁偶极子源定位海底矢量磁传感器[J]. 探测与控制学报, 2011, 33(5): 41-45, 51. Yang Mingming, Liu Daming, Lian Liting, et al.Underwater vector-magnetometer located by using magnetic dipole sources in the offing[J]. Journal of Detection & Control, 2011, 33(5): 41-45, 51. [8] 孙烨, 肖昌汉, 周国华. 磁主动式海底磁传感器定位方法及其解析公式[J]. 海洋测绘, 2012, 32(4): 25-28. Sun Ye, Xiao Changhan, Zhou Guohua, An active method and its analytical formulas of magnetic sensor positioning using the magnetic source[J]. Hydrographic Surveying and Charting, 2012, 32(4): 25-28. [9] 周国华, 张树, 赵文春, 等. 低速运动条件下的磁传感器定位方法[J]. 船电技术, 2016, 36(9): 1-5. Zhou Guohua, Zhang Shu, Zhao Wenchun, et al.Magnetic sensor positioning method under low speed movement condition[J]. Marine Electric & Electronic Engineering, 2016, 36(9): 1-5. [10] Yu Zhou, Xiao Changhan, Zhou Guohua.Multi- objectivization-based localization of underwater sensors using magnetometers[J]. IEEE Sensors Journal, 2014, 14(4): 1099-1106. [11] 王泽忠, 全玉生, 卢斌先. 工程电磁场[M]. 2版. 北京: 清华大学出版社, 2011. [12] 张朝阳, 虞伟乔. 基于磁偶极子等效的潜艇空间磁场分布[J]. 舰船科学技术, 2013, 35(1): 31-34. Zhang Zhaoyang, Yu Weiqiao.Research on spatial magnetic field distributing of submarine based on magnetic dipole equivalent[J]. Ship Science and Technology, 2013, 35(1): 31-34. [13] 刘芙妍, 颜冰. 磁偶极子阵列模型的适用性研究与优化分析[J]. 物理学报, 2022, 71(12): 124101. Liu Fuyan, Yan Bing.Applicability and optimization analysis of magnetic dipole array model[J]. Acta Physica Sinica, 2022, 71(12): 124101. [14] 李岩松, 刘启智, 刘君, 等. 基于磁偶极子模型的材料缺陷漏磁检测正演问题的单元积分计算方法[J]. 电工技术学报, 2017, 32(21): 176-185. Li Yansong, Liu Qizhi, Liu Jun, et al.The unit integral calculation method of defective material’s forward question of magnetic flux leakage detection based on the magnetic dipole model[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 176-185. [15] 丁璨, 李江, 袁召, 等. 基于NSGA-Ⅱ和BP神经网络的杯状纵磁触头结构优化设计[J]. 电工技术学报, 2022, 37(23): 6074-6082. Ding Can, Li Jiang, Yuan Zhao, et al.Structural optimization design of cup-shaped longitudinal magnetic contact based on NSGA-Ⅱ and BP neural network[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6074-6082. [16] 李婕, 杨淑英, 谢震, 等. 基于有效信息迭代快速粒子群优化算法的永磁同步电机参数在线辨识[J]. 电工技术学报, 2022, 37(18): 4604-4613. Li Jie, Yang Shuying, Xie Zhen, et al.Online parameter identification of permanent magnet syn- chronous motor based on fast particle swarm optimization algorithm with effective information iterated[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4604-4613. [17] Ye Wenxing, Feng Weiying, Fan Suohai.A novel multi-swarm particle swarm optimization with dyna- mic learning strategy[J]. Applied Soft Computing, 2017, 61: 832-843.
2024, Vol. 39 
