The number of Unmanned Aerial Vehicles (UAVs) used in various industries has increased exponentially, and abnormal detection of UAVs is one of the primary technical means to ensure that UAVs can work normally. Currently, most anomaly detection models are trained using on-board logs from drones. However, in some cases, using these logs can be problematic due to data encryption, inconsistent descriptions of characteristics, and imbalanced positive and negative samples. Consequently, the on-board logs of UAVs may not be directly usable for training anomaly detection models. Given the above problems, this paper proposes a Time Line Modeling (TLM) method based on the UAV software-in-the-loop (SITL) simulation environment to obtain and process the on-board failure logs of drones. The Time Line Modeling method includes two stages: the Fault Time Point Anchoring Method and Fault Time Window Stretching Method. First, based on the SITL simulation environment, multiple flight missions were constructed. Failures of several common components of UAVs are designed. Secondly, the fault’s initial location and end location are determined by the method of Fault Time Point Anchoring, and the original collection of tagged UAV’s on-board data is realized. Then, in terms of data processing, the features that are not universal are removed, and the flight data of the UAV is optimized by using the data balance method of Time Window Stretching to achieve the balance of normal data and abnormal data. Finally, use of algorithms such as Sequential Minimal Optimization (SMO), Random Forest (RF), and Convolutional Neural Network (CNN) were used to experiment with the processed data. The experimental results showed that the data set obtained based on this method can be effectively applied to the training of machine learning-based anomaly detection models.

With the rapid development of UAVs (Unmanned Aerial Vehicles), abnormal state detection has become a critical technology to ensure the flight safety of UAVs. The position and orientation system (POS) data, etc., used to evaluate UAV flight status are from different sensors. The traditional abnormal state detection model ignores the difference of POS data in the frequency domain during feature learning, which leads to the loss of key feature information and limits the further improvement of detection performance. To deal with this and improve UAV flight safety, this paper presents a method for detecting the abnormal state of a UAV based on a timestamp slice and multi-separable convolutional neural network (TS-MSCNN). Firstly, TS-MSCNN divides the POS data reasonably in the time domain by setting a set of specific timestamps and then extracts and fuses the key features to avoid the loss of feature information. Secondly, TS-MSCNN converts these feature data into grayscale images by data reconstruction. Lastly, TS-MSCNN utilizes a multi-separable convolution neural network (MSCNN) to learn key features more effectively. The binary and multi-classification experiments conducted on the real flight data, Air Lab Fault and Anomaly (ALFA), demonstrate that the TS-MSCNN outperforms traditional machine learning (ML) and the latest deep learning methods in terms of accuracy.

Unmanned aerial vehicles (UAVs) are susceptible to malfunctions caused by environmental and human factors. Anomaly detection is a critical technique for ensuring the safe operation of UAVs. However, traditional methods that rely on unimodal sensor data and neural networks often fail to capture the comprehensive characteristics of faulty UAVs, limiting their effectiveness in complex environments.To address these limitations, this paper proposes a UAV anomaly detection model that utilizes heterogeneous sensor data and multimodal neural networks (FMDNN). Firstly, FMDNN uses an interpolation-based method for fusing multi-source heterogeneous UAV sensor information. This method aligns the sensor data while simulating the distribution of UAV sensor data, enabling the FMDNN to utilize multi-sensor information to assess the overall state of the UAVs. Secondly, The FMDNN trains two neural networks of different modalities to extract nonlinear features and key structural features from the fused data by parallel computing using MLP and CNN, respectively. Finally, the feature fusion module combines the nonlinear and key structural features to create a fusion vector. The fusion vector is processed by the fully connected layer and used to determine the UAV's state through a Softmax classifier. Experimental results demonstrate that on the ALFA, TLM and UAVGPSAttack datasets, the FMDNN model achieves higher accuracy than the single-model classifier, by about 2% to 9%, for both multi and binary classification tasks. At the same time, the FMDNN model has strong robustness under classification tasks with noisy data.

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.

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.