UAV Flight Data Research Articles

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

A Self-Supervised Fault Detection for UAV Based on Unbalanced Flight Data Representation Learning and Wavelet Analysis

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

Flexible Mobility Models Using Stochastic Differential Equations

We develop a family of tractable models for UAV mobility. Based on a system of coupled stochastic differential equations, the resulting models exhibit realistic trajectories and flexible covariance structures, making them suitable for both small scale and large scale applications. Closed form solutions for steady state and transient position distributions, as well as position covariance, are derived. These results are subsequently used to obtain distance outage probabilities and pathloss. The analytical results allow us to assess the impact of a variety of system parameters related to device control, navigation and position measurement errors. Finally, using measured UAV flight data, we show an excellent goodness of fit between the proposed model and the data.

Research and Development of a Real-time UAV Flight Visualization Simulation System

On the premise of practical application, this paper describes the principle, technology and implementation methods of a real-time UAV flight visualization simulation system. The overall framework, hardware system composition, UAV flight model establishment, determination of flight control mode and control rate, design of 3D visual simulation system, management and processing of UAV flight state data are discussed, and finally, the actual application effect of the system is evaluated.

Development of a New Wind Measurement Method Using UAV Flight Data and Its Application to Wind Turbine Wake Measurements

Development of a New Wind Measurement Method Using UAV Flight Data and Its Application to Wind Turbine Wake Measurements

Pattern recognition of UAV flight data based on semi-supervised clustering

UAV is an unmanned aerial vehicle controlled by a remote radio signal or a trajectory planning software carried itself. It is widely used in military, civil and scientific research fields. However, due to the lack of real-time decision-making ability, the UAV has high fault rate. The flight quality assessment of UAV and the construction of fault prediction model can be used for debugging and fault-removing to customer’s aircraft, and also to increase the added value of the civilian UAV products. Before building a fault prediction model, a very important step is to identify the pattern of sampled data. For each group of flight data, the efficiency and accuracy rate of manual quality evaluation and fault identification are not acceptable. Based on the UAV flight data accumulated in the big data platform of an UAV production company in Shenyang, Liaoning Province of China, this paper proposes a semi-supervised clustering technique to do automatic pattern recognition for the sampling points. According to the characteristics of UAV flight data, two different methods are designed to choose initial centroids. Meanwhile, we use the existing normal flight data to train distance thresholds to combine some clusters to eliminate the resulting error clustering. Real flight data or flight test data with manually added labels are used to run the proposed algorithms to verify the recognition results. The experimental results show that the proposed methods greatly improve the efficiency and accuracy of adding precise labels to the historical flight data and play a role in assisting the manual recognition of sampling points while strengthening the management and statistics.

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-of- the-art method.

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ~≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positioning.

Cloud Surveillance System for UAV 3D Dynamic Operation in Remote Visualization

The unmanned aircraft system (UAS) operation demands a good surveillance system to promptly access flight information to all participating team members in missions. A system can be implemented on cloud via 3G mobile communication to uplink UAV flight data onto Internet and share with different users. In this paper, an Android smart phone is selected for data acquisition on airborne. Google Earth® 3D map is adopted to construct real time tracking display with actual attitude reference model in remote terminals. The ground computer builds MySQL database to collect downlink data and converts into user friendly format for easy access by opening browsers. With special attitude and altitude model design to match with Google Earth, the dynamic UAV performance presents very good virtual visualization to operator and observers online throughout the missions. Its record can playback to view any historical operations. This paper presents a technical realization on the UAV cloud surveillance system on UAS operations of Ce-71. The 2D attitude model demonstrates clear flight trajectory and performance, while the altitude model on 3D Google Earth offers good terrain awareness for avoidance. The system time delay and mobile coverage may limit the use of the proposed cloud surveillance for UAV. It is very helpful to mission control in better remote visualization for UAV operation.