ABSTRACT
In this paper we propose a technique to enhance the accuracy of WiFi fingerprint-based localization by imputing uncaught access point (AP) signals of WiFi fingerprints. Two techniques were developed for this; one is to impute uncaught AP signals by referring to WiFi radio map (WRM) fingerprints at the very previous location, another is referring to WRM fingerprints obtained by predicting the next location. When we measured the accuracy of localization at an E-Mart, Seoul, Korea and a KAIST Library, Daejeon, Korea with and without uncaught signal imputation, the imputed signal resulted in around 30% better accuracy improvement than the signals without imputation. In addition, the imputation methods using WRM information showed significantly better accuracy than using a fixed value for uncaught AP signals. This indicates that the uncaught signal imputation, which was overlooked in the WLAN-based localization, should be incorporated with other filtering techniques for a more reliable and accurate localization.
- Y. Chen, Q. Yang, J. Yin, and X. Chai. Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7):877--888, july 2006. Google ScholarDigital Library
- Kalman, Rudolph, and Emil. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME--Journal of Basic Engineering, 82(Series D):35--45, 1960.Google Scholar
- M. Lee and D. Han. Voronoi tessellation based interpolation method for wi-fi radio map construction. IEEE Communications Letters, 16(3):404--407, 2012.Google ScholarCross Ref
- R. Ouyang, A. Wong, and K. Woo. Indoor localization via discriminatively regularized least square classification. International Journal of Wireless Information Networks, 18:57--72, 2011. 10.1007/s10776-011-0133-5.Google ScholarCross Ref
- A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson. Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys '09, pages 85--98, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- F. Vanheel, J. Verhaevert, E. Laermans, I. Moerman, and P. Demeester. Automated linear regression tools improve rssi wsn localization in multipath indoor environment. EURASIP J. Wireless Comm. and Networking, 2011:38, 2011.Google ScholarCross Ref
- G. Welch and G. Bishop. An introduction to the kalman filter. Technical report, Chapel Hill, NC, USA, 1995. Google ScholarDigital Library
- Z.-l. Wu, C.-h. Li, J. K.-Y. Ng, and K. R. P. H. Leung. Location estimation via support vector regression. IEEE Transactions on Mobile Computing, 6(3):311--321, Mar. 2007. Google ScholarDigital Library
Index Terms
- Uncaught signal imputation for accuracy enhancement of WLAN-based positioning systems
Recommendations
Analysis of WLAN's received signal strength indication for indoor location fingerprinting
An indoor positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication ...
An extreme value based algorithm for improving the accuracy of WiFi localization
AbstractMore mobile devices can obtain WiFi Received Signal Strength (RSS) for indoor positioning, so selecting reasonable Access Points (APs) for rapid positioning is important. Furthermore, RSS is vulnerable to diverse interference, making ...
High Performance Indoor Location Wi-Fi Fingerprinting using Invariant Received Signal Strength
IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and CommunicationThe instability of Wi-Fi received signal strength (RSS) incurred by mutable channel characteristics hampers a wide-spread adoption of RSS based location fingerprinting to real world indoor localization applications. To overcome RSS instability, we ...
Comments