Wind-velocity estimation in re-entry vehicles using flush air data system and aided navigation outputs

Abstract

An algorithm for wind velocity estimation using data from Flush Air Data System (FADS) and GPS aided navigation outputs is presented in this paper. Raw estimates of wind velocity are determined using angle of attack, angle of sideslip and Mach number from FADS and GPS aided position and velocity components. These estimates are further applied to a Kalman filter to obtain optimal wind estimates. The wind velocity difference is considered to follow a first order Markov process with an initial variance of zero. Simulation studies have been carried out to validate the algorithm performance. Good match is seen between simulated wind and the optimal estimate obtained. The algorithm has been tested using data obtained from Indian Re-usable Launch Vehicle during descent phase. The results prove the effectiveness of the algorithm for wind estimation.