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Detection of malicious intent in non-cooperative drone surveillance

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Ahmad, BI 
Jahangir, M 
Godsill, S 


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.



Bayesian inference, drone, intent prediction, Kalman filtering, non-cooperative surveillance, radar

Journal Title

2021 Sensor Signal Processing for Defence Conference, SSPD 2021

Conference Name

2021 Sensor Signal Processing for Defence Conference (SSPD)

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