Experimental analysis of micro-Doppler characteristics of drones and birds for classification purposes

Bryan Tsang; Ram M. Narayanan; Ramesh Bharadwaj

doi:10.1117/12.2622408

27 May 2022 Experimental analysis of micro-Doppler characteristics of drones and birds for classification purposes

Bryan Tsang, Ram M. Narayanan, Ramesh Bharadwaj

Proceedings Volume 12108, Radar Sensor Technology XXVI; 121080K (2022) https://doi.org/10.1117/12.2622408
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

This paper investigates the use of micro-Doppler signatures of drones and birds for their detection and classification. Assessments made from simulated results are verified by data collected using a 10-GHz continuous wave (CW) radar system. Time/Velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds are used for target identification and movement classification within TensorFlow. Results using Support Vector Machine (SVM) indicate 96% accuracy for drones vs. birds and 85% accuracy among individual drone and bird distinction between 5 classes.

Conference Presentation

Citation Download Citation

Bryan Tsang, Ram M. Narayanan, and Ramesh Bharadwaj "Experimental analysis of micro-Doppler characteristics of drones and birds for classification purposes", Proc. SPIE 12108, Radar Sensor Technology XXVI, 121080K (27 May 2022); https://doi.org/10.1117/12.2622408

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