A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms

Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It designed to tackle the requirements of high-resolution and low data volume simultaneously in frequency domain. The proposed was developed from skewed distribution model self-tuning parameters density-based spatial clustering applications with noise (DBSCAN) algorithm. Specifically, band analyzed first by Jarque–Bera test. threshold determined based filter out noise. Subsequently, DBSCAN algorithm were adjusted automatically, according k-dist curve slope variation dichotomy parameter seeking algorithm, followed clustering. points as outliers. Finally, simulated experimental applied verify effectiveness method. On basis results, spectrum same resolution original obtained. amount declined more than three orders magnitude compared spectrum. presented will benefit analysis amplitude emissions.