Comparison of Speed and Accuracy of Chosen Magnetometer Calibration Algorithms in the Presence of External Interference

Abstract

The paper deals with the robustness of the calibration algorithms based on the neural networks against external interference. The chosen neural networks distinguish only in the learning rule. For a comparison the backpropagation, backpropagation with momentum and resilient propagation learning algorithms were chosen. The algorithms were examined from the calibration speed and accuracy point of view defined by the specified criterions. Tests were performed in the form of simulations with the applied measured periodical interference and the periodical interference with the random occurring amplitude modulation. The goal of the article is to prove the fundamental robustness of the algorithm against the interference and the response of the chosen algorithms to the various external interference types.