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Aerial Localization with Smartphone

  • Zhongli Liu
  • Yinjie Chen
  • Benyuan Liu
  • Jie Wang
  • Xinwen Fu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7405)

Abstract

This paper presents how we applied a smartphone for aerial localization. We have developed a fully functional aerial localization system HAWK and reported preliminary results in a related paper. In this paper, we focus on the technical details of using a smartphone Nokia N900 as a wireless sniffer on a mini helicopter and comparing the performance of three localization approaches for wireless device localization. The flight is controlled by a software controller on a laptop. The flight route can be specified in two ways: manually setting waypoints on Google map and automatically generating waypoints based on Moore space filling curve. The smartphone based sniffer captures the wireless traffic during flight and transmits the traffic dump files through a 3G network to a locator once the surveillance flight is finished. We applied three different approaches, maximum signal strength approach, centroid approach and Quasi-Newton method, for the locator on the laptop to calculate the position of the target device and compared the localization accuracy of these three localization approaches. Surprisingly, the simplest approach, maximum signal strength approach (which uses the location where the maximum signal strength is sensed as the target’s location) has similar localization accuracy compared with the other two. We also provided an indoor localization approach locating the target in a recorded video.

Keywords

Video Streaming Receive Signal Strength Indication Target Device Party Localization Software Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhongli Liu
    • 1
  • Yinjie Chen
    • 1
  • Benyuan Liu
    • 1
  • Jie Wang
    • 1
  • Xinwen Fu
    • 1
  1. 1.Computer Science DepartmentUniversity of Massachusetts LowellLowellUSA

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