An interference magnetic field compensation method based on a model-data hybrid drive

The precise compensation of the carrier’s self-interference magnetic field is a key challenge in improving the accuracy of geomagnetic navigation. Two model-data hybrid-driven magnetic interference collaborative compensation strategies are presented in response to the drawbacks of conventional compensation techniques, including the linear assumption and the challenge of responding to complex settings. In the preprocessing stage, principal component analysis is used to reduce the dimensionality of the data, and the compensation parameters obtained from the physical model are used to initialize the neural network. In scheme one, the shortcomings of the conventional compensation model are analyzed, the compensation parameters are solved using a neural network, the traditional model-solving paradigm is broken and potential multicollinearity issues are avoided, and a random factor term is introduced in the interference magnetic field to simulate the random interference magnetic field with strong nonlinearity. Scheme two constructs a loss function to link data-driven and model-driven, deeply coupling the efficient extraction of magnetic field features with the physical constraints of the interference field. Experimental results show that the proposed methods have the advantages of high compensation accuracy and strong generalization ability compared with pure model and pure data compensation methods. The highest compensation accuracy in generalization ability tests at different heights and different regions has increased by 69.64% and 69.39%, respectively, verifying the effectiveness of the proposed methods.