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Evaluation of RSSI as a Non-visual Target Tracking Technique for Drone Applications

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Advances in Information and Communication (FICC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1129))

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Abstract

Visual tracking algorithms for drone applications is a common technique for tracking and following targets. However, partial or full occlusion can cause visual trackers to lose its target or even begin following the wrong target. To combat this challenge, non-visual tracking algorithms can be used in parallel to provide more robust tracking in environments where line-of-sight cannot be guaranteed. In this study, a non-visual tracking technique using RSSI (Received Signal Strength Indicator) is evaluated for its suitability in drone applications. The study results show a RMS error of 1.17 ft. for the RSSI technique within a 10 foot radius in outdoors environments, which is an improvement over the current reported accuracy of GPS. However, interference from multipath fading in indoors settings remains a significant challenge for the RSSI technique, and modifications to the RSSI technique to mitigate multipath fading are proposed as future work.

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References

  1. Ali, Z., Landolsi, M.A., Deriche, M.A.: Comparison of derivative based and derivative free Kalman filters for multipath channel estimation in CDMA networks. In: 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4 (2009)

    Google Scholar 

  2. Chen, W., Kao, W., Chang, Y., Chang, C.: An RSSI-based distributed real-tile indoor positioning framework. In: 2019 IEEE International Conference on Applied System Invention (ICASI), pp. 1288–1291 (2018)

    Google Scholar 

  3. GPS.gov.2017.GPS Accuracy (2017)

    Google Scholar 

  4. Herrera, J.C.A., Ploger, P.G., Hinkenjann, A., Maiero, J., Flores, M., Ramos, A.: Pedestrian indoor positioning using smartphone multi-sensing, radio beacons, user positions probability map and IndoorOSM floor plan reprsenation. In: 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 636–645 (2014)

    Google Scholar 

  5. Jais, M.I., Ehkan, P., Ahmad, R.B., Ismail, I., Sabapathy, R., Jusoh, M., Rahim, H.A., Malek, M.S.: Hardware comparison capturing received signal strength indication (RSSI) for wiresless sensors network (WSN). In: 2015 IEEE Student Conference on Research and Develoment (SORReD), pp. 278–282 (2015)

    Google Scholar 

  6. Pu, C., Chung, W.: Mitigation of multipath fading effects to improve indoor RSSI performance. IEEE Sensor J. 8(11), 1884–1886 (2008)

    Article  Google Scholar 

  7. Song, J., Jeong, H., Jiang, Y., Zhang, L., Park, Y.: Improved indoor position estimation algorithm based on geo-magnetism intensity. In: 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 741–744 (2014)

    Google Scholar 

  8. Xu, L., Fang, Y., Jiang, Y., Zhang, L., Feng, C., Bao, N.: Variation of received signal strength in wireless sensor network. In: 2011 3rd International Conference on Advanced Computer Control, pp. 151–154 (2011)

    Google Scholar 

  9. Yu, H., Cho, H., Kang, C., Hong, D.: A new multipath interference mitigation techniques for high-speed packet transmission in WCDMA downlink. IEEE Signal Process. Lett. 12(9), 601–604 (2005)

    Article  Google Scholar 

  10. Zhang, K., Zhang, Y., Wan, S.: Research of RSSI indoor ranging algorithm based on Gaussian – Kalman linear filtering. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1628–1632 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Colorado, Colorado Springs, CO, USA

    Christopher Lee & Sudhanshu Kumar Semwal

Authors
  1. Christopher Lee
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  2. Sudhanshu Kumar Semwal
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Corresponding author

Correspondence to Sudhanshu Kumar Semwal .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Lee, C., Semwal, S.K. (2020). Evaluation of RSSI as a Non-visual Target Tracking Technique for Drone Applications. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1129. Springer, Cham. https://doi.org/10.1007/978-3-030-39445-5_6

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