Accuracy Enhancement with Integrated Database Construction for Indoor WLAN Localization

In this paper, we rely on the neighborhood relations of the physically adjacent Reference Points (RPs) to construct a physical neighborhood database with the purpose of enhancing the accuracy of the Receive Signal Strength (RSS) fingerprint based localization algorithms in Wireless Local Area Network (WLAN) environment. First of all, based on the Most Adjacent Points (MAPs) and their corresponding Physically Adjacent Points (PAPs), we construct the Feature Groups (FGs), and then calculate the New Reference Point (NRP) with respect to each FG. Second, the RSS at each NRP is estimated by using the least square method based surface interpolation algorithm. Finally, we apply the K Nearest Neighbor (KNN), Weighted KNN (WKNN), and Bayesian inference algorithms to locate the target. The experimental results show that the proposed integrated database construction helps a lot in improving the localization accuracy of the widely-used KNN, WKNN, and Bayesian inference algorithms.

[1]  Hadi Alasti,et al.  Efficient experimental path loss exponent measurement for uniformly attenuated indoor radio channels , 2009, IEEE Southeastcon 2009.

[2]  Feng Qiu,et al.  An Information-Based Approach to Precision Analysis of Indoor WLAN Localization Using Location Fingerprint , 2015, Entropy.

[3]  Xiang Yu,et al.  Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy , 2013, Expert Syst. Appl..

[4]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[5]  Feng Qiu,et al.  Error bound analysis of indoor Wi-Fi location fingerprint based positioning for intelligent Access Point optimization via Fisher information , 2016, Comput. Commun..

[6]  Feng Qiu,et al.  Integrated location fingerprinting and physical neighborhood for WLAN probabilistic localization , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[7]  Feng Qiu,et al.  IMLours: Indoor mapping and localization using time-stamped WLAN received signal strength , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Xiang Yu,et al.  Adaptive Mobility Mapping for People Tracking Using Unlabelled Wi-Fi Shotgun Reads , 2013, IEEE Communications Letters.

[9]  Lin Ma,et al.  A Novel Two-Step WLAN Indoor Positioning Method , 2010 .

[10]  Lin Ma,et al.  KNN-FCM hybrid algorithm for indoor location in WLAN , 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS).

[11]  Mu Zhou,et al.  Competitive agglomeration based KNN in indoor WLAN localization environment , 2015, 2015 10th International Conference on Communications and Networking in China (ChinaCom).

[12]  Feng Qiu,et al.  Correlated received signal strength correction for radio-map based indoor Wi-Fi localization , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[13]  Mu Zhou,et al.  Mobility tracking by fingerprint-based KNN/PF approach in cellular networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[14]  Mu Zhou,et al.  Personal Mobility Map Construction for Crowd-Sourced Wi-Fi Based Indoor Mapping , 2014, IEEE Communications Letters.

[15]  Mu Zhou,et al.  PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength , 2015, Sensors.

[16]  Liu Zong-tian.  Least-squares Method Piecewise Linear Fitting , 2012 .