Image of Burden Point Cloud Based on Kmeans-Bayesian Segmentation with Energy Estimation

Harsh environment inside blast furnace (BF) makes it hard to monitoring burden surface shape with heterogeneous fluidization characteristic, which is an important factor affecting the production efficiency and safety of the iron-making process. In this paper, based on the imaging principle of remote sensing Synthetic Aperture Radar (SAR) radar, we design the industrial scanning radar, multiplying the density of the sample points of the surface. By analyzing the characteristics of spectrum of BF radial echo signal, this paper proposes a new intelligent image algorithm that improves the Kmeans segmentation rectified by the minimum error Bayes decision (MEBD) to extract a strip of surface point cloud. Energy centrobaric correction method is used to estimate the distance frequency of the blast surface and the results of it are used to reconstruct 3D burden surface model. Compared with other typical methods with measured data, the presented method is verified to be more effective and robust.