Unmanned aerial vehicles (UAVs), commonly known as drones, have the potential to enable a wide variety of beneficial applications in areas such as monitoring and inspection of physical infrastructure, smart emergency/disaster response, agriculture support, and observation and study of weather phenomena including severe storms, among others. However, the increasing deployment of amateur UAVs (AUAVs) places the public safety at risk. A promising solution is to deploy surveillance UAVs (SUAVs) for the detection, localization, tracking, jamming and hunting of AUAVs. Accurate localization and tracking of AUAV is the key to the success of AUAV surveillance. In this article, we propose a novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs. At the heart of the framework is a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF). This algorithm simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation. At the same time, this algorithm leverages the advantages of fusing multi-SUAV cooperative detection to improve the algorithm accuracy. Compared with the classical interacting multiple model unscented Kalman filter (IMMUKF) algorithm, this algorithm achieves better target estimation accuracy and higher computational efficiency, and enables good adaptability in SUAV system target localization and tracking.

Date Published: 9 May 2018
PDF: 12 pages
Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520F (9 May 2018); doi: 10.1117/12.2306298

Published in SPIE Proceedings Vol. 10652:
Disruptive Technologies in Information Sciences
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)