  Modern magnetic sensor arrays conventionally utilize state of the art low
power magnetometers such as parallel and orthogonal fluxgates. Low power
fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the
fluxgate output. This phenomenon deteriorates the signal fidelity and
effectively increases the internal sensor noise. Even if sensors that are more
prone to dc jumps can be screened during production, the conventional noise
measurement does not always catch the dc jump because of its sparsity.
Moreover, dc jumps persist in almost all the sensor cores although at a slower
but still intolerable rate. Even if dc jumps can be easily detected in a
shielded environment, when deployed in presence of natural noise and clutter,
it can be hard to positively detect them. This work fills this gap and presents
algorithms that distinguish dc jumps embedded in natural magnetic field data.
To improve robustness to noise, we developed two machine learning algorithms
that employ temporal and statistical physical-based features of a pre-acquired
and well-known experimental data set. The first algorithm employs a support
vector machine classifier, while the second is based on a neural network
architecture. We compare these new approaches to a more classical kernel-based
method. To that purpose, the receiver operating characteristic curve is
generated, which allows diagnosis ability of the different classifiers by
comparing their performances across various operation points. The accuracy of
the machine learning-based algorithms over the classic method is highly
emphasized. In addition, high generalization and robustness of the neural
network can be concluded, based on the rapid convergence of the corresponding
receiver operating characteristic curves.
