Real-Time Trajectory Prediction for Urban Low-Altitude Unmanned Aerial Vehicles Based on Deep Learning Model 


Vol. 50,  No. 2, pp. 224-233, Feb.  2025
10.7840/kics.2025.50.2.224


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  Abstract

The continuous increase in global air traffic and autonomous aircraft development have made accurate trajectory prediction crucial for safe air traffic management. This study proposes a method for predicting UAV trajectories based on a deep learning model. Specifically, we propose a prediction model based on the GRU (Gated Recurrent Unit) architecture, which is well-suited for time series prediction. We applied look_back and forward_length to assess model performance across different ranges. Furthermore, to validate the performance of the proposed model, we conducted comparative experiments with RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory) models. The experimental results showed that our model achieved the best prediction performance with an RMSE of 0.0037 and demonstrated real-time prediction capability.

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  Cite this article

[IEEE Style]

D. Jang, S. Yoon, T. Park, H. Yoon, K. Lee, "Real-Time Trajectory Prediction for Urban Low-Altitude Unmanned Aerial Vehicles Based on Deep Learning Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 2, pp. 224-233, 2025. DOI: 10.7840/kics.2025.50.2.224.

[ACM Style]

Da-hyun Jang, Seung-won Yoon, Tae-won Park, Hye-won Yoon, and Kyu-chul Lee. 2025. Real-Time Trajectory Prediction for Urban Low-Altitude Unmanned Aerial Vehicles Based on Deep Learning Model. The Journal of Korean Institute of Communications and Information Sciences, 50, 2, (2025), 224-233. DOI: 10.7840/kics.2025.50.2.224.

[KICS Style]

Da-hyun Jang, Seung-won Yoon, Tae-won Park, Hye-won Yoon, Kyu-chul Lee, "Real-Time Trajectory Prediction for Urban Low-Altitude Unmanned Aerial Vehicles Based on Deep Learning Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 2, pp. 224-233, 2. 2025. (https://doi.org/10.7840/kics.2025.50.2.224)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880