QR分解与GLS改进算法在压力传感器动态性能改善中的应用

将矩形矩阵的正交（QR）分解和改进的最小二乘（GLS）算法相结合，实现传感器的动态建模，运用零极点配置理论改善传感器动态性能。利用传感器动态校准实验数据，首先构造矩形数据矩阵并对矩阵进行正交分解确定传感器模型阶次，继而利用改进的最小二乘算法建立传感器的数学模型，而后运用零极点理论对模型零极点进行重新配置改善传感器的动态性能。实验结果表明：该方法简洁，建模精度高，能够较准确得到反映传感器动态特性的数学模型，且基于该方法的传感器动态性能改善效果明显。

The application of QR decomposition and GLS improved algorithm in improving the dynamic performance of pressure sensor

Combining rectangular matrix orthogonal(QR)decomposition with improved least squares(GLS)algo?rithm ,the dynamic mathematical model of the sensor can be established and the dynamic performance of the sensor can be improved by utilizing zero-pole assignment theory. Firstly ,using the dynamic calibration test data of the pressure sensor to construct a rectangular data matrix ,the model order of sensor was determined by using the method of matrix orthogonal decomposition. Secondly ,the mathematical model of the sensor was established by using improved least squares algorithm. Lastly ,the performance of the sensor was improved by using the zero-pole assignment theory to reconfigure the zero-pole of the model. The experimental results show that the method is simple and the modeling precision is high. A mathematical model which reflecting the dynamic perfor?mance of the sensor can be obtained accurately and the dynamic performance of the sensor can be improved sig?nificantly.