In this paper, we first study the applicability and stability of the Tikhonov regularization method (TRM) and the truncated singular value decomposition method in ship’s magnetic signature inversion modeling. To further improve the noise immunity and robustness of the model, we apply an iterative regularization algorithm based on the conjugate gradient least squares (CGLS) method. Numerical simulation shows that the proposed algorithm is more applicable and can obtain better results of ship’s magnetic field extrapolation under different noise backgrounds. In the ship model experiment, the errors of the TRM and CGLS method for magnetic signature extrapolation are, respectively, 4.77% and 3.67%, showing that the proposed algorithm can improve the inversion effect of the ship model magnetic field.
Induced magnetic field (IMF) measurement is a key step in the ship’s magnetic field treatment process. In this paper, a new method for rapid measurement of longitudinal and athwartship IMF is proposed, which can improve the traditional measurement method that is either time-consuming and labor-intensive, or dependent on the magnetic field uniformity of geomagnetic simulation coils. Using the geomagnetic simulation coil in the close-wrap magnetic treatment facility, a mapping model between the coil magnetic field and the ship’s magnetized field excited by the coil is constructed in a single-heading. Based on this single-heading-measurement method, numerical simulations and hull experiments are carried out. By combining the regularization technology, the results show that the proposed method can quickly and accurately measure the ship’s IMF, and can suppress the influence of measurement error on obtaining the IMF. Compared with the geomagnetic simulation method, proposed method not only has a great calculation accuracy and strong robustness, but also requires a lower magnetic field uniformity of the geomagnetic simulation coil, and can be used as a fast emergency measurement method for ship’s IMF in engineering.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.