ABSTRACT Harmonic radar, as an emerging sensing technology, enables high‐sensitivity target detection and localization in complex environments by exploiting the nonlinear scattering characteristics of targets, such as the harmonic responses of electronic devices. Compared with conventional radar systems, it offers strong clutter suppression because most natural clutter sources lack prominent nonlinear features. However, the harmonic signals from non‐cooperative targets are extremely weak and are often buried in noise and radio‐frequency interference. In addition, harmonics generated within the radar system itself may overlap with target echoes, further complicating the detection and identification process. Under such conditions, conventional signal extraction methods not only fail to separate target signals effectively but also risk distorting them. To address this challenge, this paper proposes an adaptive signal extraction method based on harmonic characteristics. By leveraging harmonic statistical features as constraints and incorporating a two‐dimensional adaptive time‐frequency extraction mechanism, the proposed approach achieves effective separation of weak harmonic signals in complex environments. Experimental results using measured data demonstrate that this method significantly enhances the imaging quality of harmonic radar and strengthens its ability to detect and identify non‐cooperative targets.
He et al. (Thu,) studied this question.