An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network

Aeromagnetic exploration is a magnetic method that detects changes of the earth’s field by loading magnetometer on an aircraft. With miniaturization magnetometers and development unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays increasingly important role in mineral other fields due to its advantages low cost safety. However, process measurement data, ferromagnetic material aircraft itself change flight direction attitude, interference will occur affect geomagnetic magnetometer. The work compensation compensate for this part improve accuracy This paper focused problems survey data processing improved based measurement. Based Tolles–Lawson model, numerical simulation experiment UAV-based was carried out, radial basis function (RBF) artificial neural network (ANN) algorithm proposed first time data. Compared with classical backpropagation (BP) ANN, test results synthetic real measured showed RBF-ANN has higher stronger generalization ability.