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| Adaptive Trajectory Prediction Method Based on
Improved Attention Mechanism |
| HUANG Quanyin， CAI Yichao， LI Hao， TANG Xiao， WANG Chenyang |
| Air Force Early Warning Academy， Wuhan 430000， Hubei， China |
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Abstract The existing recurrent neural networks are subject to training overfitting, low prediction accuracy, poor
generalization ability, and weak adaptability in solving target trajectory prediction. A target trajectory prediction method
using an improved attention mechanism and Gated Recurrent Unit (GRU) was proposed, which could automatically
terminate the network training process through an early stopping method to prevent overfitting during training. It saved
the optimal network parameters during network training through the model checkpoint function. By introducing an
attention mechanism into the GRU network and assigning different weights to trajectory features to focus on key
trajectory information, the predictive performance of the network was optimized Finally, simulation experiments
results show that the proposed method effectively improves the prediction accuracy, generalization, and adaptability of
recurrent neural networks.
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Received: 17 April 2024
Published: 25 July 2024
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| [1] |
ZANG Hongyan, XIE Xiaolong, XU Yazhou, TAO Ye, GAO Changsheng. Hypersonic Maneuvering Target State Estimation Algorithm Based on Recurrent Neural Network[J]. Air & Space Defense, 2024, 7(4): 88-98. |
| [2] |
CHEN Wanchun, YUAN Wenjie, YU Qi, LIU Xiaoming, XU Zeng. Research on Target Classification and Trajectory Prediction Method of Airborne Interceptor Weapons Under Low Information Condition[J]. Air & Space Defense, 2024, 7(4): 81-87. |
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CAI Yunze, ZHANG Yanjun. Infrared Dim and Small Target Detection Based on Dual-Channel Feature-Enhancement Integrated Attention Network[J]. Air & Space Defense, 2021, 4(4): 14-22. |
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2024, Vol. 7 
