Time-frequency transforms for radar-based UAV recognition

Sean Kearney; Sevgi Zubeyde Gurbuz

doi:10.1117/12.2619084

13 June 2022 Time-frequency transforms for radar-based UAV recognition

Sean Kearney, Sevgi Zubeyde Gurbuz

Author Affiliations +

Proceedings Volume PC12108, Radar Sensor Technology XXVI; (2022) https://doi.org/10.1117/12.2619084
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Drone recognition has become a topic of increasing concern for defense applications. Due to the high speed of rotation of the drone blades, however, accurate drone recognition relies on sufficient time-frequency resolution of the drone radar micro-Doppler signature. Although one of the more commonly used time-frequency transforms is the spectrogram, such classical estimators embody a sub-optimal trade-off in temporal resolution versus frequency resolution. In this work, we evaluate the efficacy of various time-frequency transformations based on the latent space of deep neural networks (DNNs). In particular, we consider alternatives to the short-time Fourier transform, such as the wavelet transform, Wigner-Ville distribution, Choi-Williams distribution, and and super-resolution techniques, which have been recently shown to be effective on non-radar datasets, such as superlets. Transforms are compared for various millimeter wave radar systems for DNN-based classification.

Conference Presentation

Citation Download Citation

Sean Kearney and Sevgi Zubeyde Gurbuz "Time-frequency transforms for radar-based UAV recognition", Proc. SPIE PC12108, Radar Sensor Technology XXVI, (13 June 2022); https://doi.org/10.1117/12.2619084

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