MMF: a fault detection model for UAVs based on Multimodal Model Fusion

Abstract

Abstract Fault detection of UAVs is one of the main technical means to ensure the normal flight of UAVs. Traditional methods are based on a single model for anomaly detection, but a single model cannot learn the features of UAVs from multiple perspectives. Multi-model Neural Networks can learn features of the data from multiple perspectives. Inspired by this, a Multimodal Model Fusion (MMF) based UAV fault detection model is proposed in this paper. The MMF model consists of three parts: firstly, a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) are used to obtain nonlinear features and key features through parallel computation of homologous UAV data; secondly, key features and nonlinear features are merged to obtain a fusion vector; finally, a fully connected layer is used to compute the fusion vector, and then the results are output by a Softmax classifier. The experimental results show that the accuracy of the MMF model is higher than that of the single-model by about 2% to 7% on the ALFA dataset.