Enhanced target detection using fractional Fourier transform features with threshold‐modified normalization

Abstract

AbstractFeature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non‐normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, a target detection method that utilizes these three adjusted features is proposed. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri‐feature based detector.