Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.5555/2962686.2962691guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article
Free access

Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters

Published: 03 April 2016 Publication History

Abstract

Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.

References

[1]
Lanzisera, S.; Lin, D. T.; Pister, K. S. J., "RF Time of Flight Ranging for Wireless Sensor Network Localization," in Intelligent Solutions in Embedded Systems, 2006 International Workshop on, pp. 1--12, 30-30 June 2006
[2]
Ansari, J.; Riihijarvi, J.; Mahonen, P., "Combining Particle Filtering with Cricket System for Indoor Localization and Tracking Services," in Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, pp. 1--5, 3-7 Sept. 2007
[3]
Honkavirta, V.; Perala, T.; Ali-Loytty, S.; Piche, R., "A comparative survey of WLAN location fingerprinting methods," in Positioning, Navigation and Communication, 2009. WPNC 2009. 6th Workshop on, pp. 243--251, 19-19 March 2009
[4]
Awad, A.; Frunzke, T.; Dressler, F., "Adaptive Distance Estimation and Localization in WSN using RSSI Measures," in Digital System Design Architectures, Methods and Tools, 2007. DSD 2007. 10th Euromicro Conference on, pp. 471--478, 29-31 Aug. 2007
[5]
Shue, S.; Conrad, J. M., "A survey of robotic applications in wireless sensor networks," in Southeastcon, 2013 Proceedings of IEEE, pp. 1--5, 4-7 April 2013
[6]
Sane, T. U.; Shue, S. L. and Conrad, J. M.; "Implementation of Dynamic Source Routing using 802.15.4 on XBee Series 1 Modules," SoutheastCon 2015, Fort Lauderdale, FL, 2015, pp. 1--8.
[7]
"Omni Antenna vs. Directional Antenna", Available: http://www.cisco.com/c/en/us/support/docs/wireless-mobility/wireless-lan-wlan/82068-omni-vs-direct.html
[8]
Xu, J; Liu, W; Lang, F.; Zhang, Y. and Wang, C., "Distance Measurement Model Based on RSSI in WSN," Wireless Sensor Network, Vol. 2 No. 8, 2010, pp. 606--611.
[9]
Elango, S; Mathivanan, N and Pankaj, G. "RSSI Based Indoor Position Monitoring Using WSN in a Home Automation Application." Microprocessors and Microsystems. 2011; 11(4): 14--19.
[10]
Mehra, R.; Singh, A., "Real time RSSI error reduction in distance estimation using RLS algorithm," in Advance Computing Conference (IACC), 2013 IEEE 3rd International, pp. 661--665, 22--23 Feb. 2013
[11]
Reina, G.; Vargas, A.; Keiji Nagatani,; Kazuya Yoshida, "Adaptive Kalman Filtering for GPS-based Mobile Robot Localization," in Safety, Security and Rescue Robotics, 2007. SSRR 2007. IEEE International Workshop on, pp. 1--6, 27--29 Sept. 2007
[12]
Paul, A. S.; Wan, E. A., "RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers," in Selected Topics in Signal Processing, IEEE Journal of, vol. 3, no. 5, pp. 860--873, Oct. 2009
[13]
Kalman RE. "A New Approach to Linear Filtering and Prediction Problems." ASME. J. Basic Eng. 1960;82(1):35--45.
[14]
Faragher, R., "Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation {Lecture Notes}," in Signal Processing Magazine, IEEE, vol. 29, no. 5, pp. 128--132, Sept. 2012

Cited By

View all
  • (2017)Procedurally generated environments for simulating RSSI- localization applicationsProceedings of the 20th Communications & Networking Symposium10.5555/3107979.3107986(1-11)Online publication date: 23-Apr-2017

Index Terms

  1. Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image Guide Proceedings
        CNS '16: Proceedings of the 19th Communications & Networking Symposium
        April 2016
        56 pages
        ISBN:9781510823174

        Publisher

        Society for Computer Simulation International

        San Diego, CA, United States

        Publication History

        Published: 03 April 2016

        Author Tags

        1. RSSI
        2. distance estimation
        3. kalman filter
        4. localization
        5. location tracking
        6. wireless sensor networks

        Qualifiers

        • Research-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)264
        • Downloads (Last 6 weeks)32
        Reflects downloads up to 28 Jan 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2017)Procedurally generated environments for simulating RSSI- localization applicationsProceedings of the 20th Communications & Networking Symposium10.5555/3107979.3107986(1-11)Online publication date: 23-Apr-2017

        View Options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Login options

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media