微间隙焊缝磁光成像NN-KF跟踪算法分析

针对紧密对接微间隙焊缝，分析基于磁光成像的神经网络补偿卡尔曼滤波（kalman filtering compensated by neural network，NN-KF）跟踪算法，建立焊缝位置测量模型并运用卡尔曼滤波对焊缝位置偏差进行最优预测.卡尔曼滤波进行最优估计需建立准确的系统模型和观测模型，而在焊缝跟踪过程中，系统噪声具有非先验性.对于针对测量模型误差、过程噪声和测量噪声对卡尔曼滤波结果的影响，运用反向传播（back propagation，BP）神经网络对卡尔曼滤波结果进行修正，补偿模型误差及噪声统计不确定性造成的滤波误差.结果表明，BP神经网络补偿卡尔曼滤波算法能有效抑制滤波发散，减小噪声干扰影响，提高焊缝跟踪精度.

NN-KF of magneto-optical imaging for micro-gap seam tracking

An algorithm of Kalman filtering compensated by neural network(NN-KF) of magneto-optical imaging is researched for detecting micro-gap weld joint. A motion model is proposed and the Kalman filter is applied to predict the weld position deviation optimally. To accomplish the optimal estimation by using Kalman filter, an accurate system model and an observation model need to be established. However, the system noise has the characteristic of non-apriority in seam tracking process. Considering the influence of the motion model error, the process noise and the measurement noise on Kalman filtering, a BP neural network was employed to amend the Kalman filtering results through compensating for the filtering error caused by model error and the uncertainty of noise statistic characteristics. Experimental results demonstrate that Kalman filter compensated by a BP neural network can effectively restrain the divergence of Kalman filtering, reduce the influence of noise, and improve the seam tracking accuracy.