用CNN-LSTM-GRU混合模型精准预测高超音速导弹轨迹
Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures
- 融合CNN、LSTM与GRU，捕捉轨迹时空特征
- 显著提升高超音速导弹轨迹预测精度
- 适合国防预警与拦截系统研发人员参考
防御技术的进步对保障国家安全至关重要，尤其针对新兴威胁。高超音速导弹因极速和强机动性构成重大挑战，其轨迹的精确预测成为有效反制的关键。本文提出一种新型混合深度学习方法，结合卷积神经网络（CNN）、长短期记忆网络（LSTM）与门控循环单元（GRU），充分发挥各架构优势，成功实现对高超音速导弹复杂轨迹的高精度预测，为防御策略与导弹拦截技术提供重要支持。本研究展示了先进机器学习技术在提升防御系统预测能力方面的潜力。
原文摘要 · Abstract (English)
Advancements in the defense industry are paramount for ensuring the safety and security of nations, providing robust protection against emerging threats. Among these threats, hypersonic missiles pose a significant challenge due to their extreme speeds and maneuverability, making accurate trajectory prediction a critical necessity for effective countermeasures. This paper addresses this challenge by employing a novel hybrid deep learning approach, integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). By leveraging the strengths of these architectures, the proposed method successfully predicts the complex trajectories of hypersonic missiles with high accuracy, offering a significant contribution to defense strategies and missile interception technologies. This research demonstrates the potential of advanced machine learning techniques in enhancing the predictive capabilities of defense systems.
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