Measurements and discrimination of drones and birds with a multi-frequency multistatic radar system
Riccardo Palamà (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA))
F. Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Matthew Ritchie (University College London)
Michael Inggs (University of Cape Town)
Simon Lewis (University of Cape Town)
Hugh Griffiths (University College London)
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Abstract
This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L- and X-bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two-way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range-time plots and micro-Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal-to-noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised-learning classifiers for binary drone versus non-drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms.
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