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

Wu, Jiangxue; Chen, Xianzhong; Hou, Qingwen; Liu, Yang; Meng, Yue

doi:10.1007/978-981-32-9686-2_59

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the mechanical swing radar system, the sampled signal from one round of radar scanning can be expressed as a spectral matrix, which contains the frequency information of all the sample points on the radius of the burden surface profile. Variance extraction [15] was proposed for estimating the correlation of each sample point, and the K-means [16] method was proposed for separating false points polluted by random noise. The constant false alarm rate (CFAR) method has robust FMCW radar performance potential, and in [17], a dual-focus SAR imaging (DfSAR) and fusion algorithm combined with CFAR was proposed.…”

Section: Introductionmentioning

confidence: 99%

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

Ni,

Chen,

Hou

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

show abstract

Section: Introductionmentioning

confidence: 99%