Jesus David Terrazas Gonzalez
A pilot study on aeronautical surveillance system for drone delivery using heterogeneous software defined radio framework.
Gonzalez, Jesus David Terrazas; Fung, Wai-Keung
This paper presents a heterogeneous computing framework to interface single board computers (SBC) to (i) distinct type of computing nodes, (ii) distinct operating systems, and (iii) distinct software applications for aeronautical surveillance system for drone delivery. The implementation platform selected is the Beagle Bone Black (BBB) having the operating system (OS) Linux Ubuntu 14. The computing nodes the BBB interfaces to are: (i) a personal laptop (MacBook Pro), (ii) a virtual machine, and (iii) two servers with distinct OSs. The software applications the BBB interfaces to are: (i) Gqrx, (ii) GNURadio, (iii) Google Earth, (iv) systems took kit (STK), and (v) Matlab. This heterogeneous computing framework, with the potential for incorporating specialized processing and networking capabilities, allows scalability for system integration to existing surveillance system for manned aircrafts. The proposed system successfully decodes the location of aircraft in real-time.
|Start Date||Jul 14, 2017|
|Publication Date||Mar 12, 2018|
|Publisher||Institute of Electrical and Electronics Engineers|
|Institution Citation||TERRAZAS GONZALEZ, J.D. and FUNG, W.-K. 2017. A pilot study on aeronautical surveillance system for drone delivery using heterogeneous software defined radio framework. In Shen, Y., Ming, A. and Wu, X. (eds.) Proceedings of the 2017 IEEE international conference on real-time computing and robotics (RCAR 2017), 14-18 July 2017, Okinawa, Japan. New York: IEEE [online], article number 8311912, pages 499-504. Available from: https://doi.org/10.1109/RCAR.2017.8311912|
|Keywords||Beagle bone black; Heterogeneous computing; Networking; Single board computer; SBC; Software Defined Radio; SDR; STK; RTLSDR|
GONZALEZ 2017 A pilot study on aeronautical surveillance
You might also like
Algorithms and methods for video transcoding.
Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms.