Conference paper Open Access

Qualitative and quantitative validation of drone detection systems

Doroftei, Daniela; De Cubber, Geert

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    <subfield code="a">Unmanned Aerial Vehicles</subfield>
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    <subfield code="a">Drones</subfield>
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    <subfield code="a">Detection systems</subfield>
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    <subfield code="a">Drone detection</subfield>
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    <subfield code="a">Test and evaluation methods</subfield>
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    <subfield code="a">Qualitative and quantitative validation of drone detection systems</subfield>
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    <subfield code="a">System  for  detection of  Threat Agents in  Maritime Border  Environment</subfield>
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    <subfield code="a">&lt;p&gt;As drones are more and more entering our world, so comes&amp;nbsp;the need to regulate the access to airspace for these systems. A necessary tool in order to do this is a means of detecting these drones. Numerous commercial and non-commercial parties have started the development of such drone detection systems. A big problem with these systems is that the evaluation of the performance of drone detection systems is a difficult operation, which requires the careful consideration of all technical and non-technical aspects of the system under test. Indeed, weather conditions and small variations in the appearance of the targets can have a huge difference on the performance of the systems. In order to provide a fair evaluation and an honest comparison between systems, it is therefore paramount that a stringent validation procedure is followed. Moreover, the validation methodology needs to find a compromise between the often contrasting requirements of end users (who want tests to be performed in operational conditions) and platform developers (who want tests to be performed that are statistically relevant). Therefore, we propose in this paper a qualitative and quantitative validation methodology for drone detection systems. The proposed validation methodology seeks to find this compromise between operationally relevant benchmarking (by providing qualitative benchmarking under varying environmental conditions) and statistically relevant evaluation (by providing quantitative score sheets under strictly described conditions).&lt;/p&gt;</subfield>
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