Multi-Model Fusion-Based Abnormal Trajectory Correction Method for Unmanned Aerial Vehicles

WANG Wei; SHE Dingchen; WANG Jiaqi; HAN Dairu; JIN Benzhou

doi:10.11999/JEIT241026

Volume 47 Issue 5

May 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(5): 1332-1344

WANG Wei, SHE Dingchen, WANG Jiaqi, HAN Dairu, JIN Benzhou. Multi-Model Fusion-Based Abnormal Trajectory Correction Method for Unmanned Aerial Vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1332-1344. doi: 10.11999/JEIT241026

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WANG Wei, SHE Dingchen, WANG Jiaqi, HAN Dairu, JIN Benzhou. Multi-Model Fusion-Based Abnormal Trajectory Correction Method for Unmanned Aerial Vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1332-1344. doi: 10.11999/JEIT241026

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Multi-Model Fusion-Based Abnormal Trajectory Correction Method for Unmanned Aerial Vehicles

doi: 10.11999/JEIT241026 cstr: 32379.14.JEIT241026

1.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Funds: The National Natural Science Foundation of China (62371231), The Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu (BK20222001), The Jiangsu Provincial Key Research and Development Program (BE2023027)

Received Date: 2024-11-18
Rev Recd Date: 2025-03-13

Available Online: 2025-03-21

Publish Date: 2025-05-01

Abstract

Abstract

Objective The opening of low-altitude airspace and the widespread deployment of Unmanned Aerial Vehicles (UAVs) have significantly increased low-altitude flight activities. Trajectory planning is essential for ensuring UAVs operate safely in complex environments. However, wireless remote control links are vulnerable to interference and spoofing attacks, leading to deviations from planned trajectories and posing serious safety risks. To mitigate these risks, UAV position parameters can be predicted and used to replace erroneous navigation system values, thereby correcting abnormal trajectories. Existing prediction-based correction methods, however, exhibit low efficiency and error accumulation over long-term predictions, limiting their practical application. To address these limitations, this study proposes a multi-model fusion method to improve the efficiency and accuracy of abnormal trajectory correction, providing a robust solution for real-world UAV operations. Methods An Long Short-Term Memory (LSTM)-Transformer prediction model, integrating LSTM and Transformer, is proposed to exploit the strengths of both architectures in time series forecasting. LSTM efficiently captures short-term dependencies in sequential data, whereas Transformer is well-suited for modeling long-term dependencies. By combining these architectures, the proposed model enhances the capture of both short-term and long-term dependencies, reducing prediction errors. The overall framework of the LSTM-Transformer prediction model is illustrated in (Fig. 3). The input time series data undergoes preprocessing before being fed into the LSTM and Transformer sub-models, each generating a corresponding feature vector. These feature vectors are concatenated and further processed by a fully connected layer to extract intrinsic data features, ultimately producing the prediction results. To further optimize the model, a blockwise attention strategy is proposed. The detailed computation process is shown in (Fig. 4). During self-attention calculations in the Transformer sub-model, the input sequence is divided into multiple sub-blocks, allowing for parallel computation. The results are then concatenated to obtain the final output. This approach effectively reduces the computational complexity of the Transformer sub-model while improving the efficiency of abnormal trajectory correction. The blockwise attention strategy not only enhances computational efficiency but also maintains prediction accuracy, making it a crucial component of the proposed method. Results and Discussions Experiments are conducted using a public dataset to predict UAV positional parameters, including longitude, latitude, and altitude. The dataset’s feature parameters are presented in (Table 1). The trajectory correction performance of the proposed method is evaluated and compared with other correction methods using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). (Fig. 5) and (Fig. 6) present the error metrics of the proposed method in comparison with Support Vector Regression (SVR), CNN-LSTM, and LSTM-RF under different prediction step sizes and measurement noise standard deviation conditions. The results indicate that the proposed method achieves the lowest correction errors. At a prediction step size of 20 and a measurement noise standard deviation of 0.19, the proposed method achieves RMSE, MAE, and MAPE values of 0.2971, 0.2208, and 21.688%, respectively. Compared with SVR, CNN-LSTM, and LSTM-RF, the RMSE is reduced by 39.52%, 6.22%, and 20.65%, the MAE by 45.5%, 8.46%, and 20.52%, and the MAPE by 8.955%, 2.03%, and 3.532%, respectively. (Fig. 7) and (Fig. 8) compare the proposed method with the original LSTM-Transformer, the Transformer with the blockwise attention optimization strategy, and individual LSTM and Transformer models in terms of error metrics under different prediction steps and measurement noise standard deviation conditions. When the prediction step is 20 and the measurement noise standard deviation is 0.19, the proposed method achieves RMSE reductions of 12.23%, 4.07%, 1.36%, and 3.48%, MAE reductions of 19.36%, 6.76%, 3.83%, and 4.21%, and MAPE reductions of 3.84%, 3.616%, 2.075%, and 2.087%, compared to the other four correction methods. These findings demonstrate the superior performance of the proposed method in reducing trajectory correction errors. The runtime efficiency of the proposed method under different prediction steps is evaluated, as shown in (Fig. 9). With a prediction step size of 20, the proposed method completes the prediction in 0.699 s, which is 35.87% faster than the original LSTM-Transformer model. This confirms that the blockwise attention optimization strategy enhances correction efficiency. Finally, (Fig. 10) presents trajectory comparisons, illustrating the accuracy of the proposed method. The predicted trajectories closely align with actual trajectories, outperforming baseline methods in correcting UAV abnormal trajectories under various conditions. Conclusions The proposed multi-model fusion method for UAV abnormal trajectory correction enhances correction efficiency and reduces errors more effectively than benchmark methods. The results demonstrate that the method achieves accurate and reliable trajectory correction, making it suitable for practical UAV applications.

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References(33)

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