Rocket engine experimental data reconstruction based on compressive sensing with MOD dictionary

The purpose of this article is to solve the problem of reconstructing the under sampling data achieved in the rocket engine experiment. This paper briefly introduces the problems of rocket engine experimental data processing and the theoretical basis of the application of the compressive sensing method based on the MOD (optimal direction method) dictionary in reconstructing the under sampling data in rocket engine experiment. Several groups of numerical experiments based on different CS methods were carried out to reconstruct the pressure signals from a certain electric propulsion rocket engine, and through the reconstruction results, we can see that the reconstruction signal based on MOD dictionary has a high precision and the sampling, storage and computing cost can be reduced greatly. The comparison between experimental results demonstrates the application value of the compressive sensing method based on MOD dictionary in the under sampling data reconstruction of rocket engine experiment.