Electromagnetic Signature Reduction of Ferromagnetic Vessels Using Machine Learning Approach.

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]