Oncu Seckin, Karakaya Mehmet, Dalveren Yaser, Kara Ali, Derawi Mohammad
TUBITAK BILGEM, Ankara 06100, Turkey.
Department of Electrical and Electronics Engineering, Gazi University, Ankara 06570, Turkey.
Sensors (Basel). 2024 Dec 4;24(23):7776. doi: 10.3390/s24237776.
This 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.
本文对使用软件定义无线电（SDR）平台的实时雷达分类进行了全面评估。在SDR的推动下，从模拟技术向数字技术的转变彻底改变了无线电系统，通过基于软件的操作提供了前所未有的灵活性和可重新配置性。这一进步补充了雷达信号参数的作用，这些参数封装在脉冲描述字（PDW）中，在电子支援措施（ESM）系统中起着关键作用，能够检测和分类威胁雷达。本研究提出了一种基于SDR的雷达分类系统，该系统以更高的处理速度实现实时操作。该系统采用基于密度的带噪声空间聚类（DBSCAN）算法作为强大的分类器，利用图形处理单元（GPU）并行化来高效提取射频（RF）参数。实验结果突出了这种方法的效率，在高达200 MSps的采样率下运行时，处理速度有显著提高，实时雷达分类的准确率达到89.7%。
