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Magnetic Signature Prediction for Efficient Degaussing of Naval Vessels
IEEE Transactions on Magnetics ( IF 2.1 ) Pub Date : 2020-09-01 , DOI: 10.1109/tmag.2020.3010421
Ankita Modi , Faruk Kazi

Accurate magnetic-signature prediction is the most challenging task in the path of active magnetic-signature minimization. This article discusses the effect of the change in hull thickness, relative permeability, and radius of the object, considering the detailed understanding of the magnetic signatures. The traditional magnetic experiment-based method using a 12-coil structure is implemented for a double hull vessel. However, the time required and effort involved in predicting the magnetic signature is much higher. Hence, a multiple linear regression-based statistical machine-learning (ML) technique for the magnetic signature prediction is proposed. The nonlinear magnetic behavior of the naval vessel is assumed to be linear, because the hysteresis curve in the range of the earth’s ambient field is linear. Hence, a linear relationship is developed between the magnetic signatures and each component of the ambient earth’s magnetic field. The ML-based model also considers the crosstalk among the longitudinal, athwartship, and vertical signatures. The predicted magnetic signatures using the ML technique and the experiment-based techniques are compared. The ML method is found to be accurate, fast, and precise compared with the laboratory-based method, which has strict and continuous requirements for hardware magnetic facility. This method is very simple to implement and directly contributes to the development of the real-time degaussing current calculation algorithm.

中文翻译:

用于海军舰艇有效消磁的磁特征预测

准确的磁特征预测是主动磁特征最小化路径中最具挑战性的任务。本文考虑到对磁特征的详细理解,讨论了船体厚度、相对磁导率和物体半径变化的影响。传统的基于磁性实验的方法使用 12 线圈结构,用于双壳船。然而,预测磁特征所需的时间和精力要高得多。因此,提出了一种用于磁特征预测的基于多元线性回归的统计机器学习 (ML) 技术。海军舰艇的非线性磁性能被假定为线性的,因为在地球环境场范围内的磁滞曲线是线性的。因此,在磁特征和地球周围磁场的每个分量之间建立了线性关系。基于 ML 的模型还考虑了纵向、横向和垂直特征之间的串扰。比较使用 ML 技术和基于实验的技术预测的磁特征。与基于实验室的方法相比,ML 方法被发现准确、快速和精确,后者对硬件磁设施有严格和持续的要求。这种方法实现起来非常简单,直接有助于实时消磁电流计算算法的发展。和垂直签名。比较使用 ML 技术和基于实验的技术预测的磁特征。与基于实验室的方法相比,ML 方法被发现准确、快速和精确,后者对硬件磁设施有严格和持续的要求。这种方法实现起来非常简单,直接有助于实时消磁电流计算算法的发展。和垂直签名。比较使用 ML 技术和基于实验的技术预测的磁特征。与基于实验室的方法相比,ML 方法被发现准确、快速和精确,后者对硬件磁设施有严格和持续的要求。这种方法实现起来非常简单,直接有助于实时消磁电流计算算法的发展。
更新日期:2020-09-01
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