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

基于云模型和多目标规划的FADS系统测量精度的研究

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

Received: 23 December 2020

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%.

摘要

为了提升一种先进的新型机载传感器——嵌入式大气数据传感器（flush air data sensing，FADS）的测量精度，以正态云模型和多目标规划（multi-objective programming，MOP）为出发点，在原有的"三点法"基础上提出一种新的改进方法。基于CFD软件得到的数据库和亚音速及超音速情况下的空气动力学知识建立高精度FADS系统模型，利用正态云模型对测量信号的不确定性和随机性进行量化分析，在对系统冗余信号的融合过程中，基于多目标规划中的松弛变量法和拉格朗日乘子法提出一种新的计算客观权重方法。仿真结果表明，与传统方法相比，新提出的基于云模型和多目标规划的方法可将测量精度提升3.2%，测量数据的离散程度降低68.88%。