Abstract
In this paper, a Bayesian approach is proposed for the early detection of a drone threatening or anomalous behaviour in a surveyed region. This is in relation to revealing, as early as possible, the drone intent to either leave a geographical area where it is authorised to fly (e.g. to conduct inspection work) or reach a prohibited zone (e.g. runway protection zones at airports or a critical infrastructure site). The inference here is based on the noisy sensory observations of the target state from a non-cooperative surveillance system such as a radar. Data from Aveillant’s Gamekeeper radar from a live drone trial is used to illustrate the efficacy of the introduced approach.
| Original language | English |
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| Title of host publication | 2021 Sensor Signal Processing for Defence Conference (SSPD) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Number of pages | 5 |
| ISBN (Electronic) | 9781665433143 |
| ISBN (Print) | 9781665433150 (PoD) |
| DOIs | |
| Publication status | Published - 23 Sept 2021 |
| Event | 2021 Sensor Signal Processing for Defence: SSPD 2021 - Edinburgh, United Kingdom Duration: 14 Sept 2021 → 15 Sept 2021 |
Publication series
| Name | Sensor Signal Processing for Defence (SSPD) |
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Conference
| Conference | 2021 Sensor Signal Processing for Defence |
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| Country/Territory | United Kingdom |
| City | Edinburgh |
| Period | 14/09/21 → 15/09/21 |
Bibliographical note
Acknowledgment:Authors thank Aveillant for providing real radar data and supporting this work. We acknowledge DSTL’s financial support via DASA (Countering Drones Phase 1) under contract DSTLX1000144447.