Abstract

<h3>Abstract</h3> Filtering-based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self-contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao-Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle-of-Arrival measurements in particle-filter-based positioning improves the 3-D positioning accuracy by about 37.3% on average.

Highlights

  • Indoor positioning plays an increasingly important role in our daily life and industry

  • Time difference of arrival (TDOA) is the measurement generally used in ultra wideband (UWB)-based positioning

  • The Chan-Taylor algorithm estimates positions based on the rule of Weighted Least Squares (WLS), and it aims to find out a single optimal solution that can best fit each constraint equation by minimizing the sum of the squares of residuals

Read more

Summary

Introduction

Indoor positioning plays an increasingly important role in our daily life and industry. The indoor positioning applications of the targets in the second category include pedestrian tracking in shopping malls, goods tracking in logistics, and process monitoring in car-smartmanufacturing factories, etc UWB transmits a radio signal over a wide portion of frequencies based on short pulses (nanosecond level) This property has the advantages for indoor positioning such as high-ranging accuracy (Sahinoglu et al, 2008), very high-penetrating power (Geng et al, 2005), less interference from multipath effect (Sahinoglu et al, 2008), high-speed data transmission (Mitchell & Kohno, 2003), and extremely low power consumption (Gezici & Poor, 2009). Both TDOA and AOA measurements are processed in the LE

Methods
Findings
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.