A Multi-Point Distance-Bounding Protocol for Securing Automatic Dependent Surveillance-Broadcast in Unmanned Aerial Vehicle Applications

The Automatic Dependent Surveillance-Broadcast(ADS-B)protocol is being adopted for use in unmanned aerial vehicles(UAVs)as the primary source of information for emerging multi-UAV collision avoidance algorithms.The lack of security features in ADS-B leaves any processes dependent upon the information vulnerable to a variety of threats from compromised and dishonest UAVs.This could result in substantial losses or damage to properties.This research proposes a new distance-bounding scheme for verifying the distance and flight trajectory in the ADS-B broadcast data from surrounding UAVs.The proposed scheme enables UAVs or ground stations to identify fraudulent UAVs and avoid collisions.The scheme was implemented and tested in the ArduPilot SITL(Software In The Loop)simulator to verify its ability to detect fraudulent UAVs.The experiments showed that the scheme achieved the desired accuracy in both flight trajectory measurement and attack detection.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Air traffic monitoring using optimized ADS-B CubeSat constellation

The primary technique used for air traffic surveillance is radar.However,nowadays,its role in surveillance is gradually being replaced by the recently adopted Automatic Dependent Surveillance-Broadcast(ADS-B).ADS-B offers a higher accuracy,lower power consumption,and longer range than radar,thus providing more safety to aircraft.The coverage of terrestrial radar and ADS-B is confined to continental parts of the globe,leaving oceans and poles uncovered by real-time surveillance measures.This study presents an optimized Low-Earth Orbit(LEO)-based ADS-B constellation for global air traffic surveillance over intercontinental trans-oceanic flight routes.The optimization algorithm is based on performance evaluation parameters,i.e.,coverage time,satellite availability,and orbit stability(precession and perigee rotation),and communication analysis.The results indicate that the constellation provides ample coverage in the simulated global oceanic regions.The constellation is a feasible and cost-effective solution for global air supervision,which can supplement terrestrial ADS-B and radar systems.

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems(6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system-Automatic Dependent Surveillance-Broadcast(ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods.Finally, we conclude this paper with a discussion of open problems in this area.