IPSJ SIG Technical Report Vol.2014-UBI-43 No /7/28 Wi-Fi 1 1 Wi-Fi Wi-Fi Wi-Fi Wi-Fi Boosted Robust Wi-Fi Fingerprinting based on Boosting Consi
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1 Wi-Fi 1 1 Wi-Fi Wi-Fi Wi-Fi Wi-Fi Boosted Robust Wi-Fi Fingerprinting based on Boosting Considering Signal Strength Stability of APs Taniuchi Daisuke 1 Maekawa Takuya 1 Abstract: This paper proposes a new Wi-Fi based indoor positioning method that is robust over unstable Wi-Fi access points (APs). Because Wi-Fi based indoor positioning relies on unstable and uncontrollable infrastructure (Wi-Fi APs), the positioning performance significantly decreases when such unstable APs are included in the localization system. This paper proposes a indoor positioning method by employing ensemble of weak position estimators, which permits us to construct a robust positioning model. Our proposed boosted position estimator has the following features. 1) The estimator does not overfit the training data and thus it is robust over unstable signals from APs. 2) Because each weak estimator employs a small number of APs for positioning, the estimator does not affected by the curse of dimensionality. 3) Our model can adaptively change the weight (importance) of each weak estimator according to a user s position in order to achieve a position-aware precise localization. Keywords: Indoor Positioning, Fingerprinting, Curse of Dimensionality, Boosting 1. 1 Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan GPS Wi-Fi LAN 1
2 GPS GPS A-GPS GPS Wi-Fi Wi-Fi Wi-Fi Wi-Fi [1], [2] Wi-Fi Wi-Fi Wi-Fi MAC Wi-Fi Wi-Fi Wi-Fi Wi-Fi Wi-Fi Wi-Fi Wi-Fi Wi-Fi Boosted Boosted 2
3 Wi-Fi 2 Wi-Fi Wi-Fi 2.1 Wi-Fi Wi-Fi Wi-Fi Naive BayesSVMk knn: k-nearest Neighbor algorithmgmm: Gaussian Mixture Model [3] Wi-Fi top-k 2.2 Gallagher [4] Lim [5] 2.3 Wi-Fi S. Chen [6] Y.C. Chen [7] Yin [8] Wi-Fi Wi-Fi Wi-Fi Pan [9] Wi-Fi Manifold co-regularization [] 3
4 Wi-Fi スキャン 弱位置推定器 1 AP1, AP2, AP7, AP11, AP15, AP1AP2AP3 エリア推定器 弱位置推定器の重みの決定 弱位置推定器 2 AP2, AP4, AP7, AP10, AP12, 弱位置推定器 3 AP1, AP3, AP4, AP15, AP19, 重み付け平均 最終的な推定座標 Boosted 位置推定器 1 Fig. 1 Outline of proposed method. [] Boosted [] ( 1 ) ( 2 ) Wi-Fi ( 3 ) (1)(2) (3) Wi-Fi 3.2 k Wi-Fi Wi-Fi Wi-Fi Wi-Fi Wi-Fi x i x i Euc(x, x i ) Euc(x, x i ) = x x i = AP ( ) 2. xj x i j AP x j j top-k P os(x) P os(x) k i=1 P os(x) = Euc(x, xi ) 1 P os i k i=1 Euc(x,. xi ) P os i i P os(x) 3.3 Boosted Boosted Boosted k j 4
5 3.4 3 A n 3 w n = S j F j. V j i x i A j AP j (x i Est n) x i A i AP j x i n (Est n ) j S j V j F j AP j S j = 1 N V j = 1 N F j = N i (x i AP j) A N i (x i AP j) A N i (x i AP i) A x i j. ( x i j S j ) 2. (1/N). x i j xi AP j N A 3.5 P os(x) P os(x) = n w n P os n (x). w n n w n P os n (x) n Wi-Fi Google Galaxy Nexus 2 2 Wi-Fi 2 Wi-Fi 28 Wi-Fi k k = Boosted k k = %k 2.93m 5
![3.00 [ m 2.50 2.00 1.50 1.00 0.50 2.93 2.29 2.14 2.24 2.52 2.14 2.49 2.47 2.44 ] 0.00 knn N S F V S & F S & V F & V S & F & V 2 29.8m 16.3m Fig. 2 Floor plan of experimental environment (29.](/docs-images/95/125329837/images/6-0.jpg "8m 1 Table 1 16.3m). Comparison of positioning error distances of our proposed methods with various parameters. AP % 10 20 30 40 50 60 5 3.56 3.57 3.04 2.69 2.85 2.70 10 3.07 2.42 2.30 2.25 2.32 2.")
6 3.00 [ m ] 0.00 knn N S F V S & F S & V F & V S & F & V m 16.3m Fig. 2 Floor plan of experimental environment (29.8m 1 Table m). Comparison of positioning error distances of our proposed methods with various parameters. AP % Boosted Boosted 30% 40% 30 30% k k = 3 3 Fig. 3 Comparison among various weighting methodologies. 3 3 N SV F k 2.93m2.29m SF V F k k = 3 k 6
7 4 Fig. 4 [ m ] [ m ] knn (S) (S; w/o ) k k = Fig. 5 1 The comparison among our proposed method, the same method without defining areas, and the existing knn (k = 3) method knn S k k = 3 Transitions of accuracy related to knn (k = 3) method and our proposed method. 0.7m 0.06m k k = 3 28 k k Boosted Wi-Fi [1] Liu, H., Darabi, H., Banerjee, P. and Liu, J.: Survey of wireless indoor positioning techniques and systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 37, No. 6, pp (2007). [2] Kaemarungsi, K. and Krishnamurthy, P.: Modeling of indoor positioning systems based on location fingerprinting, INFOCOM 2004., Vol. 2, pp (2004). [3] Saha, S., Chaudhuri, K., Sanghi, D. and Bhagwat, P.: Location determination of a mobile device using IEEE b access point signals, Wireless Communications and Networking (WCNC 2003), Vol. 3, pp (2003). [4] Gallagher, T., Li, B., Dempster, A. G. and Rizos, C.: Database updating through user feedback in fingerprintbased Wi-Fi location systems, Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS 2010), IEEE, pp. 1 8 (2010). [5] Lim, J., Jang, W., Yoon, G. and Han, D.: Radio Map Update Automation for WiFi Positioning Systems, Communications Letters (2013). [6] Wang, Y., Jia, X., Lee, H. and Li, G.: An indoors wireless positioning system based on wireless local area network infrastructure, 6th Int. Symp. on Satellite Navigation Technology Including Mobile Positioning & Location Services, No. 54 (2003). [7] Seshadri, V., Zaruba, G. V. and Huber, M.: A bayesian sampling approach to in-door localization of wireless devices using received signal strength indication, PerCom 2005, pp (2005). [8] Bahl, P. and Padmanabhan, V. N.: RADAR: An inbuilding RF-based user location and tracking system, IN- FOCOM 2000., Vol. 2, pp (2000). [9] Chan, L.-w., Chiang, J.-r., Chen, Y.-c., Ke, C.-n., Hsu, J. and Chu, H.-h.: Collaborative localization: Enhancing WiFi-based position estimation with neighborhood links in clusters, Pervasive Computing 2006, Springer, pp (2006). 5. Wi-Fi 7
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