Detection of Malicious Intent in Non-cooperative Drone Surveillance

被引：4
|
作者：
Liang, Jiaming [1 ]
Ahmad, Bashar I. [2 ]
Jahangir, Mohammad [3 ]
Godsill, Simon [1 ]
机构：
[1] Univ Cambridge, Engn Dept, Cambridge, England
[2] Aveillant Thales Land & Air Syst, Cambridge, England
[3] Univ Birmingham, Dept Elect Elect & Syst Engn, Birmingham, England
来源：
2021 SENSOR SIGNAL PROCESSING FOR DEFENCE CONFERENCE (SSPD) | 2021年
关键词：
Bayesian inference; drone; intent prediction; Kalman filtering; non-cooperative surveillance; radar; DESTINATION PREDICTION; TRACKING;
D O I：
10.1109/SSPD51364.2021.9541485
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：80 / 84
页数：5
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