Hypersonic Target Recognition Based on the Space-Based Hyperspectral Detector With Deep Learning

Deep learning has become a key research component of space target detection. To ensure the safety of the aerospace field with the rapid development of hypersonic vehicles, the deep learning method is applied to recognize the flight state and type of hypersonic targets (HTs) for the first time in this article. It makes two main contributions. First, a method for constructing HT datasets based on the space-based low-orbit hyperspectral detector is proposed, which uses the nonequilibrium high-temperature flow field generated during the flight of HTs to calculate the spectral radiation characteristics and simulate the intraclass variation of each subclass of hyperspectral targets in the actual observation scene. Second, an in-depth study on the performance of deep learning methods using the constructed HT dataset is conducted, as well as a comparison with a recognition benchmark using machine learning and deep learning methods is discussed. Finally, we summarize the challenges faced by HT recognition.