Abstract
In this work, we show how drone detection and classification can be enabled by leveraging a database of radar cross section (RCS) signatures. First, we present a set of measurement results of the RCS of a carbon fiber drone model at 28 GHz. The measurements were performed in an anechoic chamber and provide essential information about the RCS signature of the specific drone. Then, we assess the RCS-based detection probability and the range error by running simulations in urban environments. The drones were positioned at different distances, from 30m to 90m, and the RCS signatures used for the detection and classification were obtained experimentally.
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