Entry Navigation

Entry navigation focuses on precisely determining and controlling a spacecraft's trajectory during atmospheric entry, crucial for successful planetary landings and missions. Current research emphasizes using machine learning, particularly neural networks (including LSTMs) and deep learning models like YOLOv5 and Faster R-CNN, to improve the accuracy of atmospheric density estimation and autonomous navigation based on onboard sensor data (e.g., cameras, accelerometers). These advancements are vital for enhancing the reliability and autonomy of space missions, enabling more complex maneuvers and reducing reliance on ground-based tracking. Improved navigation algorithms also support applications like on-orbit servicing and space debris removal.