Unmanned Aerial Vehicles Communication Interference Detection Based on SSA-BP Neural Network

Abstract

This paper proposes a method for detecting communication interference in unmanned aerial vehicles (UAVs). First, we train the BP neural network with signal data from interference attacks, and then continuously optimize the BP neural network using the sparrow search algorithm (SSA). After iterations, we obtain the final interference detection model. With this detection model, we can detect whether there are malicious interference signals in the drone flight environment and evaluate the detection model using the detection rate. Finally, our detection model has an accuracy of up to 93.64%.