Distributed Remote Sensing of Hypersonic Vehicle Performance based on Sonic Boom

Sonic boom signals contain a lot of useful performance information of hypersonic vehicles, such as lift-to-drag ratio, vehicle sizes, Mach numbers & heights over the entire flight profile, and jet plumes, etc. However, the sonic boom research during the past half century only focused on the low-boom design for supersonic and hypersonic vehicles without leveraging the hidden information for other purposes. Spectral Energies, LLC (SE) and Prof. Suo Yang (University of Minnesota) propose to develop distributed remote sensing technology for tracking adversary hypersonic flight vehicle performance. The concept is to utilize the large amount of information hidden in the far-field sonic boom signals from a large number of (currently available in commercial and military airplanes and ships) sensing sources to infer adversary hypersonic vehicle performance without need of close reconnaissance, which is beyond the capacity of other approaches such as satellite images, and is very robust to sense any hypersonic vehicles at any flight speeds and heights. Specifically, based on computational and experimental data, semi-analytic theories, machine learning (ML) and artificial intelligence (AI), analysis software with new algorithms to decipher the large number of sonic boom signals will be developed to provide vehicle performance information. The goal is to efficiently utilize the sonic boom signals and recover as much useful vehicle performance information as possible.