Beam-Steering Radar Imaging for Blast Furnace Burden Surface Based on Sparse Bayesian Learning in Time-Constrained Scenario

Accurate measurement of the burden surface plays a crucial role in optimizing blast furnace charging, reducing carbon emissions, and decreasing energy consumption. During the production process, the burden distribution cycle dynamically changes. When abnormal conditions occur in the blast furnace, the burden charge cycle is shortened, creating a time-constrained scenario for measurement of the burden surface. Increasing the speed of beam steering leads to the problem of sparse aperture in radar echoes. Sparse aperture results in a lack of radar echo consistency, leads to the “scallop” effect and affecting the accuracy of reconstruction. To address the problem, we propose a novel Bayesian imaging method for burden surface based on the beam steering radar mechanism. Firstly, the mechanism model of beam steering radar and Gaussian-Laplace mixed guidance prior are introduced, and the measurement matrix of beam steering radar is proposed. Secondly, an alternative function is introduced to approximate the Gaussian likelihood, which leads to a relaxation of the posterior density of the model parameters to improve the speed of reconstruction. Finally, the accuracy and efficiency of the proposed fast algorithm are verified by experimental results obtained from measured data. The experimental results on the W-shaped burden surface show that our method achieves 49%, 74%, and 56.1% improvements in RMSE, MAE, and Entropy, respectively, compared to the traditional sparse Bayesian learning method.