Improved Trajectory Prediction and Simplification Analysis for ATM System Modernization

Souza, Eric Conrado de; Fattori, Caio Cesar; Silva, Suely

doi:10.1016/j.ifacol.2017.08.1900

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…This, in turn, has an impact on the prediction accuracy. As a matter of fact, the factors that affect the aircraft trajectory are very complex, including the computation model, aircraft intent, environmental conditions and performance parameters [6][7][8], etc. While most of relevant parameters are difficult to measure accurately, the usage of aircraft performance models is limited to some ideal conditions due to inaccuracy and instability in real data testing.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Yang

Tang

Bao

et al. 2020

Sensors

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This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

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Section: Related Workmentioning

confidence: 99%