Exploring the measurement accuracy of flush air data sensing based on normal cloud model and multi-objective programming

Abstract

As far as airborne sensors are concerned, the measurement accuracy is an important indicator that cannot be ignored and may directly affect final measurement results. In order to improve the measurement accuracy of a flush air data sensing (FADS), which is an advanced sensor, this paper proposed a new method based on the normal cloud model and the multi-objective programming (MOP). First, the high-precision FADS model is established by using the database obtained with the CFD software and aerodynamics knowledge. Meanwhile, the uncertainty and randomness of signals caused by measurement noise are quantitatively analyzed by using the normal cloud model. Then, in the process of data fusion, a new method for calculating the weights is proposed based on the slack variable method and the Lagrange multiplier method. The simulation results show that the proposed method can improve the measurement accuracy by 3.2% and reduce the dispersion of measurement data by 68.88%.

Keywords

• flush air data sensing (fads)
• normal cloud model
• multi-objective programming (mop)
• slack variable method
• lagrange multiplier method