Temperature compensation methods of spin-exchange relaxation-free co-magnetometer

Abstract According to the temperature characteristics of spin-free exchange relaxation (SERF) co-magnetometer, three temperature compensation methods are proposed in this paper, including particle swarm optimization radial basis function (PSO-RBF) neural network, Gaussian regression and least squares support vector basis. The effectiveness of the three compensation methods is verified by experiments and compared with the back-propagation (BP) neural network optimized by a genetic algorithm compensation method. In order to improve the effect of temperature compensation, this paper also conducts correlation and cluster analysis on the different positions temperature and output signals of the SERF co-magnetometer, and selects the data of temperature points that are closely related to signal bias changes for model training. Through experimental comparison with traditional linear compensation and BP neural network compensation methods, it is found that PSO-RBF neural network has advantages in training speed, compensation accuracy and robustness. Experiments show that PSO-RBF neural network temperature compensation algorithm improves the stability of the SERF co-magnetometer by more than 53 % at room temperature or under artificially imposed temperature changes.