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Detection of multiple micro-drones via cadence velocity diagram analysis

Detection of multiple micro-drones via cadence velocity diagram analysis

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Many studies have demonstrated the capability of radar micro-Doppler signature for classifying micro-drones. However, most existing works on radar classification of drones are based on the assumption that the received signal is only reflected from a single drone. When multiple drones are present simultaneously, the existing methods of drone classification fail due to the superimposition of the micro-Doppler features of multiple drones. In this Letter, a method for detection of multiple drones is proposed. First the time–frequency spectrogram is converted into the cadence-velocity diagram (CVD), which expresses how the curves in the time-frequency-domain repeat. Then the cadence frequency spectrum (CFS), as the basis vector of the training data from each class, of the CVD is extracted. Finally, the K-means classifier is used to recognise the component of multiple micro-drones based on the CFS. The experimental results on real radar data demonstrate that the proposed method is capable of dealing with multiple drones with satisfactory classification accuracy.

References

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      • 5. Vishwakarma, S., Ram, S.S: ‘Classification of multiple targets based on disaggregation of micro-Doppler signatures’. IEEE Proc. Asia-Pacific Microwave Conf., New Delhi, India, December 2016, doi: 10.1109/APMC.2016.7931360.
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      • 6. Björklund, S., Hohansson, T., Petersson, H.: ‘Evaluation of a micro-Doppler classification method on mm-wave data’. Proc. IEEE Radar Conf., Atlanta, GA, USA, May 2012, pp. 09340939.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.4317
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drone detection
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