Automated Line-Based Sequential Sampling and Modeling Algorithm for EMC Near-Field Scanning

In this paper, a novel algorithm that selects optimal paths for conducting automated near-field (NF) measurements is presented. The resulting dataset of measurements can then be used to model the complete NF electromagnetic emissions of an electronic device or predict the far-field emissions. The models obtained using the training sets generated with the aid of the proposed algorithm are substantially more accurate compared to existing point-based methods. The algorithm is validated by comparing it against an earlier adaptive sampling algorithm that optimizes point-based measurement datasets.