Review of Autonomous Navigation Methods for Hypersonic Aircraft Using Inertial Navigation Sensors Systems and Machine Learning

Abstract

This paper presents a study on improving the design and performance of hypersonic aircraft through targeted modifications of critical components. The focus areas include autonomous navigation systems, sensor integration modules, trajectory control mechanisms, and real-time communication technologies. The research is structured into four stages: component identification, CAD modeling, incorporation of modifications, and simulation and analysis. Each stage employs rigorous methodologies, such as utilizing SolidWorks for CAD modeling and C++ OpenGL Flight Simulator for performance simulation. The modifications led to significant improvements in key performance metrics. The autonomous navigation system had an increase in accuracy due to advanced algorithms and artificial intelligence integration. Sensor modules saw an enhancement in data resolution and reliability, providing more precise real-time monitoring of aerodynamic forces, thermal loads, and structural integrity. The trajectory control mechanisms were strengthened with the use of advanced materials and innovative designs, ensuring stability and control at hypersonic speeds. Real-time communication systems were optimized, resulting in a reduction in latency and an increase in bandwidth, enabling efficient data exchange and dynamic mission control. These enhancements translate to improved overall efficiency, speed, and adaptability of the hypersonic aircraft, addressing critical challenges in high-speed aviation. The study's findings highlight the potential for these modifications to revolutionize hypersonic flight by providing more reliable, efficient, and adaptable solutions. The research contributes to the broader context of hypersonic aircraft design, paving the way for future advancements and practical applications in both military and commercial high-speed aviation.