Oncu, SeckinKarakaya, MehmetDalveren, YaserKara, AliDerawi, Mohammad2025-03-202025-03-2020241424-8220https://doi.org/10.3390/s24237776https://hdl.handle.net/20.500.14034/2230This paper presents a comprehensive evaluation of real-time radar classification using software-defined radio (SDR) platforms. The transition from analog to digital technologies, facilitated by SDR, has revolutionized radio systems, offering unprecedented flexibility and reconfigurability through software-based operations. This advancement complements the role of radar signal parameters, encapsulated in the pulse description words (PDWs), which play a pivotal role in electronic support measure (ESM) systems, enabling the detection and classification of threat radars. This study proposes an SDR-based radar classification system that achieves real-time operation with enhanced processing speed. Employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a robust classifier, the system harnesses Graphical Processing Unit (GPU) parallelization for efficient radio frequency (RF) parameter extraction. The experimental results highlight the efficiency of this approach, demonstrating a notable improvement in processing speed while operating at a sampling rate of up to 200 MSps and achieving an accuracy of 89.7% for real-time radar classification.eninfo:eu-repo/semantics/openAccesssoftware-defined radioelectronic support measuresradar classificationparameter extractionclusteringGPUArticle10.3390/s242377762423Q2WOS:0013781726000012-s2.0-8521179953039686314Q1