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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. [ABSTRACT FROM AUTHOR]

This paper proposes a fast and efficient method for magnetic signature reduction of underwater vessels. The magnetic signature reduction is required to protect the ferromagnetic vessels from magnetic anomaly detectors and mines. We propose a novel machine learning-based approach for degaussing of the vessel. This method adds a degree of bias to the evaluated coefficients in order to handle inherent multicollinearity issues. The proposed algorithm is efficient in terms of computational efforts, speed, and accuracy. More than 90% of signature reduction is achieved, assuming that the signature predicted is accurate. The proposed method is validated for a simulated model of prototype submarine. [ABSTRACT FROM AUTHOR]