Processing of magnetic scalar gradiometer signals using orthonormalized functions

This work relates to the detection of hidden ferromagnetic objects with the use of a gradiometer that comprises two scalar sensors and functions as a magnetic anomaly detector (MAD). A hidden object is represented by a magnetostatic dipole. Modeling of the MAD output signal is carried out by its decomposition in the space of orthogonal functions (an orthonormal basis) constructed with the use of Gram-Schmidt algorithm. A set of five functions is found to be sufficient for an accurate signal description in a wide range of distances between the gradiometer and the dipole. The dipole energy signal is introduced in the basis chosen and is found to be a useful function for the data processing algorithm based upon the results of the modeling. It is shown that the use of this function improves either the signal-to-noise ratio or the detection characteristics. Moreover, the dipole energy signal turns out to be independent of the dipole orientation. This leads to the possibility of using an identical signal processing algorithm for all variety of dipole waveforms.