基于神经网络的体视PIV空间标定模型

体视粒子图像测速（SPIV）中的空间标定精度对SPIV的测试结果精度有较大影响。为研究标定模型对输入误差的处理能力，定义了一个无量纲参数——误差衰减系数，来评判空间标定模型对误差的响应。在此基础上针对SPIV两相机空间标定的误差产生和传播特性，发展了一种基于神经网络的且具有联合标定能力的SPIV空间标定模型。使用仿真实验手段，证实了该神经网络模型在很大的参数空间内均具有对输入误差的抑制能力，而传统的多项式模型或小孔模型并不具备这一能力；此外，神经网络模型在高光学畸变情况下的表现也优于多项式模型及小孔模型。因此，神经网络具备替换传统空间标定模型的能力，有助于提高SPIV的测量精度。最后在实验中证实了神经网络标定模型的空间定位误差仅为传统模型的1/4。

Spatial calibration model of stereo PIV based on neural network

The accuracy of spatial calibration in Stereo Particle Image Velocimetry (SPIV) has a considerable influence on the accuracy of velocity measurement. To study the ability of various calibration models to handle input errors, we define a dimensionless parameter, namely, the error attenuation coefficient, to evaluate the response of spatial calibration models to input errors. Based on this error attenuation coefficient, the error propagation characteristics of conventional spatial calibration models, including the polynomial model and the camera pinhole model, can be quantitatively evaluated. A neural network-based space calibration model is then developed. Unlike conventional calibration models, this new model is naturally adaptive to multiple-camera joint calibration, thus suitable for SPIV. Synthetic experiments demonstrate the ability of this neural network model to suppress the propagation of the input error in a large measurement parameter space, which is not possessed by the polynomial model or the pinhole model. Additionally, it outperforms traditional models in the scenario of high optical distortion. Therefore, this neural network model might be an ideal candidate for the spatial calibration of SPIV. Finally, it is confirmed in the experiment that the error of neural network calibration model is only a quarter of that of traditional models.