Using Data Fusion to Analyze Dynamic Stability of Atmospheric Entry Vehicles

Atmospheric entry and descent is one of the most important stages of a space mission. After a successful entry, the vehicle decelerates with the aerodynamic forces exerted on the body. As the vehicle decelerates, it starts to oscillate due to the aerodynamic nature of the blunt bodies. However, a safe landing often requires parachute deployment which can be done within a certain oscillation frequency and amplitude. Unless these oscillations are addressed during the design stage, they may lead to catastrophic events, such as unsuccessful parachute deployment or tumbling during descent. As a result, identifying the dynamic characteristics of the vehicle is crucial for the safety and success of the mission. In this study, we are proposing a data fusion algorithm to estimate the dynamic stability coefficients of an atmospheric entry vehicle by using two different datasets representing the numerical and experimental results. The algorithm uses the trajectory of the vehicle as the input data and estimates the dynamic stability coefficients based on the angle of attack oscillations. The proposed algorithm consists of three steps: (i) the reconstruction of the trajectory based on the sparse observation points representing the experimental data, (ii) the numerical and experimental data fusion, and (iii) the dynamic stability coefficient estimation by using the Markov Chain Monte Carlo method.