Adaptive cancellation of geomagnetic background noise for magnetic anomaly detection using coherence

Abstract

Magnetic anomaly detection (MAD) is an effective method for the detection of ferromagnetic targets against background magnetic fields. Currently, the performance of MAD systems is mainly limited by the background geomagnetic noise. Several techniques have been developed to detect target signatures, such as the synchronous reference subtraction (SRS) method. In this paper, we propose an adaptive coherent noise suppression (ACNS) method. The proposed method is capable of evaluating and detecting weak anomaly signals buried in background geomagnetic noise. Tests with real-world recorded magnetic signals show that the ACNS method can excellently remove the background geomagnetic noise by about 21 dB or more in high background geomagnetic field environments. Additionally, as a general form of the SRS method, the ACNS method offers appreciable advantages over the existing algorithms. Compared to the SRS method, the ACNS algorithm can eliminate the false target signals and represents a noise suppressing capability improvement of 6.4 dB. The positive outcomes in terms of intelligibility make this method a potential candidate for application in MAD systems.