Magnetic Anomaly Detection Using Noise-Optimized Orthonormalized Functions on Dual Magnetometric Sensor Signals

The performances of classic matched filter approaches for magnetic dipole detection are limited due to the correlated nature of the environmental magnetic noise. For this reason, several studies have proposed background noise optimization of the classic matched filter, but only for single-sensor applications. In this study, we extend the noise-optimized matched filter for the detection of a ferromagnetic object to dual-sensor scalar magnetometric applications, and we provide the associated formal development. The noise-optimized matched filter is built by whitening the classic orthonormalized basis functions of the original matched filter by the sampled background noise covariance matrix, according to the minimum variance distortionless response criterion. The receiver operating characteristic curves show a +2 dB processing gain between the original matched filter and the noise-optimized one when they are applied to a ferromagnetic object signal placed in a colored noise environment. Moreover, for dual-sensor signals, an additional array gain of +3 dB is observed in comparison with a single magnetic sensor, meaning that the proposed method has a total gain of +5dB compared to the original matched filter applied to a single-sensor signal. These results are encouraging for the use of magnetometric array processing techniques in the future.