Radar Error Calculation and Correction System Based on ADS-B and Business Intelligent Tools

Zuluaga, Jimmy Anderson Flórez; Vargas-Bonilla, Jesús Francisco; Pabón, José David Ortega; Rios, Carlos Mario Suarez

doi:10.1109/ccst.2018.8585728

Cited by 6 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional surveillance technologies, such as primary surveillance radars and secondary surveillance radars [6]- [8], have obvious technical limitations in meeting requirements of future dense air traffic management (ATM) such as high operating costs, limited coverage, and relatively low accuracy of tracking [9]- [11]. To address these challenges, an automatic dependent surveillance-broadcast (ADS-B) technique has been proposed to enhance the surveillance architecture [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

Cheng

Guo

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles' security issues pose a major challenge to the Air-Ground Integrated Vehicle Networks (AGIVN). Traditional surveillance technology lacks the capacity to support the intensive air traffic management (ATM) of the future. Therefore, an advanced automatic dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this paper, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B dataset, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the longterm trajectory of aerial vehicles. And the conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

show abstract