多新息理论优化卡尔曼滤波焊缝在线识别

针对间隙小于0.05 mm的低碳钢对接焊缝，用磁光传感方法获取焊缝位置信息，研究多新息理论优化卡尔曼滤波在焊缝识别及跟踪中的应用.在获取磁光图像及提取焊缝位置的过程中存在较多干扰，而传统卡尔曼滤波受噪声的影响较大，难以对焊缝偏差进行最优估计.为此，结合多新息理论，提出一种焊缝位置检测的卡尔曼滤波改进算法，在对当前时刻进行预测时，充分考虑之前多个时刻的运动状态，综合历史数据估计出焊缝位置信息，对不同新息值进行试验比较并考虑计算量和滤波精度，发现选用两个新息值优化卡尔曼滤波算法可得到较好的效果.结果表明，多信息理论优化卡尔曼滤波算法可有效提高焊缝位置检测精度.

Kalman filtering optimized by multi-innovation theory for on-line weld detection

Amagneto-optical sensor was used to capture the images of butt joint weld of low carbon steel plate whose gap is less than 0.05 mm. In order to detect and track the weld,Kalman filtering which is optimized by a multi-innovation theory was applied to the magneto-optical image that contains the weld location information. Because of the noise interferences in acquiring magneto-optical images and data extracting, the standard Kalman filtering that is easily affected usually can not work efficiently. Therefore it is difficult to make an optimal estimation for the deviation in seam tracking by using the standard Kalman filtering. Based on the multi-innovation theory, an optimized Kalman filtering algorithm was proposed to detect the weld. When the present weld position is predicted, this optimized Kalman filtering algorithm was fully applied for the useful information of previous moment and historical data to estimate the weld location information. Experiments using different innovation values were carried out and the amount of calculation and filtering precision was considered. It was found that the optimized Kalman filtering algorithm could obtain better effect by using two innovation values. Experimental results showed that the seam tracking accaracy could be improved via using Kalman filtering optimized by multi-innovation theory.