Research progress on the influence of aero-optics on precision of starlight navigation and correction strategies

Significance Starlight navigation is a fully autonomous navigation technology with advantages such as strong anti-interference capability, no error accumulation over time, and high navigation accuracy. However, when the vehicle flies at high speed through the atmosphere, aero-optical effects cause distortion in the target images received by the vehicle's imaging sensor, thus compromising navigation accuracy. The escalating demands for navigation precision in high-speed vehicles have propelled aero-optical effects into the spotlight, attracting significant and growing interest within the research community. This review focuses on the impact of aero-optical effects on the accuracy of starlight navigation and the enhancement of imaging quality.Progress First, the fundamental principles of aero-optical effects and their influence (Fig.1). Then, correction methods for aero-optical effects are summarized from the perspectives of suppressing effect generation and restoring degraded images (Fig.2). Additionally, the influence of key geometric parameters of the optical guide head on aero-optical effects is also analyzed (Fig.3). Following that, the current status, challenges, and potential of digital image processing techniques in mitigating image degradation due to aero-optical effects are discussed. Finally, the future prospects for the application of aero-optical effect correction methods are outlined based on existing literature.Conclusions and Prospects Faced with the challenges of algorithm applicability and real-time performance, it is imperative to emphasize the importance of prior knowledge in algorithm development. An image correction algorithm framework that relies on prior knowledge should be established, ensuring that this knowledge is closely integrated with key parameters in the correction process. Building on this, future starlight navigation technology should actively explore the deep integration of novel optical transmission mechanisms and intelligent algorithms. For example, by combining the physical properties of spatiotemporal vortex beams with deep learning-based correction methods, a new paradigm of “physics-algorithm” synergistically enhanced starlight navigation can be constructed. Spatiotemporal vortex beams possess orbital angular momentum and self-healing characteristics, which can provide more robust optical input signals for deep learning models. Simultaneously, neural networks can learn the distortion patterns of vortex beams in aero-optical environments to establish more accurate inverse correction models, enabling high-precision wavefront reconstruction and enhanced restoration of navigation images. Furthermore, continued advancement in optical materials and active thermal management technologies should be promoted to facilitate the integrated design of optical windows. It is also recommended to strengthen the integration of starlight navigation with other navigation methods to construct a multi-layer, fault-tolerant integrated navigation system. Through continued breakthroughs in these directions, starlight navigation technology is expected to play a more critical role in next-generation high-speed vehicles and deep-space exploration missions.