지능형 레이더 시스템 및신호처리 연구실

This domain turns raw radar returns into trustworthy intermediate representations: SAR/ISAR formation, motion compensation, autofocus, 3D rotational compensation and cross-range scaling, super-resolution, and polarimetric or interferometric scattering analysis. We move beyond the monostatic limit through bistatic, multistatic and MIMO interferometric ISAR geometries and W-band synchronization across distributed platforms, recovering resolution from sparse apertures via compressive sensing and deep super-resolution. Our research focuses on intelligent radar-based perception for detecting and recognizing targets in complex remote-sensing environments. Radar and synthetic aperture radar (SAR) provide reliable sensing capabilities over long distances and under diverse environmental conditions, making them well suited for large-scale surveillance and remote sensing. However, variations in sensing geometry, background clutter, and target appearance often make robust interpretation of radar imagery challenging. To address these challenges, we study methods for object detection and feature enhancement that emphasize informative target signatures while suppressing irrelevant clutter. Our research explores deep learning-based representation learning, target-aware feature extraction, and clutter-robust processing to improve the localization and characterization of objects in radar imagery. These approaches aim to provide more discriminative and reliable features for subsequent recognition. Electronic warfare (EW) research focuses on countering anti-ship missiles equipped with monopulse radar seekers through various electronic attack (EA) techniques and expendable countermeasures such as chaff and decoys. We also investigate electronic protection (EP) techniques against hostile ele