Integrating ground surveillance with aerial surveillance for enhanced amateur drone detection

Jian Wang; Xuejun Yue; Yongxin Liu; Houbing Song; Jiawei Yuan; Tianyu Yang; Remzi Seker

doi:10.1117/12.2304531

9 May 2018 Integrating ground surveillance with aerial surveillance for enhanced amateur drone detection

Jian Wang, Xuejun Yue, Yongxin Liu, Houbing Song, Jiawei Yuan, Tianyu Yang, Remzi Seker

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 106520E (2018) https://doi.org/10.1117/12.2304531
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Unmanned Aerial Systems (UASs) are becoming increasingly popular for amateur use, but their arbitrary deployment poses severe public safety threats to critical infrastructures, such as airports. Typically an Amateur Unmanned Aerial System (AUAS) communicates with a ground control station (GCS) through a telemetry radio, which keeps transmitting data in poor connection conditions. The accuracy of AUASs detection is of great significance. In this paper, we propose a novel surveillance framework which leverages Surveillance Unmanned Aerial Systems (SUASs) to detect AUASs. The approximate position of an AUAS is first estimated by Ground Surveillance Nodes (GSNs) with radio receivers, and SUASs are then activated to determine its precise position. Different from previous research, this framework not only leverages both ground and aerial surveillance capabilities, but also integrates both radio and image processing techniques, thus achieving enhanced AUAS detection capability. This platform has the potential to be integrated with other advanced technologies, providing the recognition of radio signals and imagery for a holistic solution of effective AUAS detection.

Conference Presentation

Citation Download Citation

Jian Wang, Xuejun Yue, Yongxin Liu, Houbing Song, Jiawei Yuan, Tianyu Yang, and Remzi Seker "Integrating ground surveillance with aerial surveillance for enhanced amateur drone detection", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520E (9 May 2018); https://doi.org/10.1117/12.2304531

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