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CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors

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Abstract

Indoor localization using a Received Signal Strength Indicator (namely, RSSI localization) has been considered a poor measurement for target tracking. The main cause of this inaccurate measurement is that RSSI’s behaviors heavily depend on environmental factors. That is, one significant challenge to localization using RSSI is that the strength of a signal varies with the environment confounding wireless communications power and signal control. In this paper, we propose Circular RSSI And Multi-Sector tracking (CRAMStrack), a novel approach to reducing the uncertainty of RSSI localization by modifying the relationship of RSSI-to-Distance (RtD), based on the sectors of a circle and the position of the tracked target. Traditional RSSI tracking uses one uniform RtD relationship to locate a target whereas CRAMStrack utilizes multiple RtD responses for each wireless sensor. The paper examines CRAMStrack’s tracking ability in a Euclidean space with estimation techniques. Real-world experiments demonstrate CRAMStrack in a testbed environment to locate targets in both stationary, linear, and non-linear movement patterns with single and group-based formations. The track accuracy was about 1.46m for moving targets, while CRAMStrack had a 40% reduction in Root Mean Square Error (RMSE) over Uni-RtD using neighboring sensor information.

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Notes

  1. This relationship will be labeled as “Uni-RtD” throughout the paper as referring to the Uniform RtD tracking relationship.

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Acknowledgements

This research was started through the 2014 summer research program of the Air-Force Research Laboratory (AFRL) in Rome, NY with a Dynamic Data Driven Application Systems (DDDAS) AFOSR grant 88ABW-2014-1321. We would like to thank Pieter Becue, Brecht Vermeulen, Vincent Sercu, and Bart Jooris for their diligent work in maintaining and supporting the iMinds w-iLab.t testbed that has made possible to conduct the real-world experiments in this paper. Tommy Chin would also like to thank Dr. Bo Yuan, Dr. Sumita Mishra, Liz Zimmerman, and Megan Fritts for their help during my graduate study in the Computing Security Department at the Rochester Institute of Technology. Finally, Dr. Kaiqi Xiong’s research has been supported in part by National Science Foundation (NSF) under his grants #1633978, #1620871, #1620862, and #1620868 and by NSF/BBN under grants CNS#1125515 for project #1895 and CNS#1346688 for project #1936.

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of Air Force Research Laboratory, National Science Foundation, or the U.S. Government.

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Authors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Tommy Chin

  2. University of South Florida, Tampa, FL, USA

    Kaiqi Xiong

  3. U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, Toledo, OH, USA

    Eric Blasch

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  1. Tommy Chin
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Correspondence to Tommy Chin.

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Chin, T., Xiong, K. & Blasch, E. CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors. J Sign Process Syst 93, 79–97 (2021). https://doi.org/10.1007/s11265-020-01516-3

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