A UAV classification system based on FMCW radar micro-Doppler signature analysis

• Published in: Expert systems with applications Vol. 132; pp. 239 - 255
• Main Authors: Oh, Beom-Seok, Guo, Xin, Lin, Zhiping
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.10.2019; Elsevier BV
• Subjects: Airborne surveillance radar; Automotive parts; Classification; Continuous radiation; Dwell time; Empirical analysis; Empirical mode decomposition; Error analysis; Feature extraction; Micro-Doppler signature; Pattern analysis; Pruning; Radar echoes; Radar signatures; Signature analysis; Surveillance FMCW radar; Traffic surveillance; UAV classification; Unmanned aerial vehicles; Surveillance FMCW radar; Empirical mode decomposition; Micro-Doppler signature; UAV classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.05.007

•An efficient and effective UAV classification system using FMCW surveillance radar echo signals.•Micro-Doppler signature analysis on FMCW radar echoes from UAV.•UAV classification results on physically measured FMCW radar echo signals. Due to its small size, slow flying speed, and low flying altitude, classification of mini-sized unmanned aerial vehicles (UAVs) using a frequency-modulated continuous wave (FMCW) surveillance radar is a challenging task. This is because the FMCW radar echo signals are acquired at a short dwell time and thus contain limited information about targets. In this paper, we first analyze FMCW radar returns from various types of UAVs and non-UAV objects in terms of the micro-Doppler signature (m-DS) pattern. Based on the analysis results, we propose an effective and efficient UAV classification system using FMCW radar echo signals. The proposed system consists of five main parts namely, (i) burst selection, (ii) rule-based scan pruning, (iii) the empirical mode decomposition based m-DS analysis and features extraction, (iv) error counting minimization based class label estimation, and (v) scan-to-scan filtering. Our experimental results on physically measured FMCW radar echo signals from several types of UAVs and non-UAV objects show that the proposed system consistently outperforms a commercial-off-the-shelf UAV classification system in terms of the classification accuracy.