Using an artificial neural network to improve the orientation accuracy of unmanned aerial vehicles

Abstract

The importance of Magnetometer measurement in all the fields that are related to moving bodies including UAV, they are used for calculation the attitude of moving body, they are also can integrated with other sensors to calculate accurate position moving body. According to that, there is an urgent need to get accurate data from magnetometer, the raw data can be used directly or can be processed by a calibration process. In this article, an assessment of using neural network (NN) in calibration magnetometer is conducted, the NN model is a sequential model that consists of many layers, the dataset contains raw measurements and calibrated data. The model achieved learning accuracy 99.38 % and loss 1.411 ; the loss metric is mean square error. The dataset was split into three parts, train, test and validation. Train and validation parts are used in the training process. The trained model is tested on the test part, on the whole dataset and on noisy dataset by adding some noise on raw data and test the efficiency of NN model. The results showed high efficiency of NN model in calculating calibrated output directly from raw measurements. A structure of NN is also discussed. To validate the proposed method in the field of UAV orientation, heading angle by using magnetometer data is calculated for raw, calibrated, output of proposed method and another calibration method on the same dataset