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TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips.

Xiaolong Li1,2, Yan Zheng3, Jun Cai4

  • 1The School of Computer Science and Information Security, Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China. xlli@guet.edu.cn.

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|June 16, 2017
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Summary
This summary is machine-generated.

TrackCC is a new wireless indoor localization system (ILS) using low-power chips. It achieves decimeter-level accuracy, outperforming existing systems in both line-of-sight and non-line-of-sight scenarios.

Keywords:
cheap communication chipdiscrete power outputspattern matchingsignal fluctuationwireless indoor localization system

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Area of Science:

  • Wireless communication
  • Localization systems
  • Signal processing

Background:

  • Commercial low-power System-on-Chips (SoCs) like nRF24LE1 present challenges for indoor localization due to limited power levels and signal fluctuations.
  • Existing wireless indoor localization systems (ILS) like LANDMARC and SAIL offer meter-level accuracy, which can be insufficient for many applications.

Purpose of the Study:

  • To propose a novel wireless indoor localization system (ILS) named TrackCC.
  • To overcome the practical design challenges posed by low-power SoCs for accurate positioning.
  • To achieve decimeter-level positioning accuracy in diverse indoor environments.

Main Methods:

  • Exploiting Markov theory to develop a state transition matrix for mitigating signal fluctuations.
  • Implementing a priority-based pattern matching algorithm for efficient position estimation.
  • Utilizing commercial nRF24LE1 low-power System-on-Chips (SoCs) for the TrackCC system.

Main Results:

  • TrackCC demonstrates decimeter-level average positioning accuracy.
  • The proposed system significantly outperforms existing meter-level accuracy ILSs (LANDMARC, SAIL).
  • Effective performance is achieved in both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions.

Conclusions:

  • TrackCC offers a robust and accurate solution for wireless indoor localization using affordable hardware.
  • The integration of Markov theory and pattern matching effectively addresses SoC limitations.
  • The system provides a viable alternative for applications requiring high-precision indoor positioning.