Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures

TL;DR

This paper introduces a hybrid deep learning model combining CNN, LSTM, and GRU architectures to accurately predict hypersonic missile trajectories, significantly improving defense response capabilities.

Contribution

It presents a novel integrated deep learning approach specifically designed for complex hypersonic missile trajectory prediction, advancing current defense technology methods.

Findings

High accuracy in trajectory prediction demonstrated

Effective integration of CNN, LSTM, and GRU architectures

Potential to enhance missile interception systems

Abstract

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.