GPR-Based EMI Prediction for UAV's Dynamic Datalink

Abstract

The datalink of an unmanned aerial vehicle (UAV) is vulnerable to external electromagnetic interference (EMI). The characteristic parameters of the EMI signal can be definitively detected by a developed monitoring platform mounted on the UAV, but it is impossible to determine the lost-link thresholds of the datalink in any state by experiment because the state of the datalink varies dynamically with the flight distance and the UAV's attitudes. In this case, whether a certain state of the UAV's datalink will be interrupted or not by the EMI cannot be evaluated. In this article, a method of EMI prediction for the UAV's dynamic datalink based on the Gaussian process regression (GPR) is proposed, two indicators, including the interfering frequency and the operation signal power of the datalink, are taken as the training inputs, and the lost-link thresholds of the datalink obtained by the EMI injection test are taken as the training targets. The results show that this method can effectively fit the training samples and determine a 95% confidence interval of the predictive outputs, which has a low predictive error, less than 2.2 dB. Compared with the methods of the layer recurrent neural network and support vector machine, the GPR method has higher predictive accuracy and a stronger generalization ability. Therefore, the detected EMI characteristic parameters can be used as new input samples to predict the lost-link thresholds in any condition. On this basis, the EMI margin can be calculated to evaluate the degree of the anti-EMI redundancy of the UAV's datalink, which can be used for the active adaptation of the UAV's datalink to EMI in the future.