Real-time Reduction of Magnetic Noise Associated with Ocean Waves via Sage-Husa Algorithm for Towed Overhauser Marine Geomagnetic Sensor

Based on Weaver's model, we analyze the magnetic noise variations of the towed Overhauser marine magnetic sensor associated with the different ocean depths, wave periods and amplitudes in theory, proving the necessity of suppressing the magnetic noise in the extreme ocean conditions. In this paper, we propose a novel real-time reduction method of the magnetic noise via an improved Sage-Husa adaptive Kalman filter (KF) to improve the sensor sensitivity. The simulation results show that the proposed method could achieve the fast convergence of the covariance of magnetic noise without the priori noise statistics or real-time reference noise. Furthermore, the dependency on the initial parameters is reduced compared with the standard Sage-Husa algorithm. In addition, we implement a towed Overhauser marine magnetometer to test the proposed method. Over all, the comparison experiments show that the proposed method not only realizes the adaptive estimation of the magnetic noise statistics, but also has better suppression effect than the standard KF. The power spectral density of the magnetic noise is reduced to 6 pT/Hz^1/2@1Hz from 50 pT/Hz^1/2@1Hz after flitting.