IPSJ SIG Technical Report Vol.2014-DPS-160 No /7/25 Fingerprint LAN LAN Fingerprint Fingerprint LEIFD (Location Estimation method that Int
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1 Fingerprint LAN LAN Fingerprint Fingerprint LEIFD (Location Estimation method that Integrates Fingerprint and Dead reckoning) 13% Fingerprint Indoor Location Estimation integrated Fingerprint and DeadReckoning Abstract: In recent years, with the price reduction of Wi-Fi devices,wi-fi devices be used in variou places. In addition, with the spread of smartphone, Indoor location-based service by smartphone is increasing. As a exsisting approach about Indoor location estimation, there is a Hidden Markov Model, the hybrid method using Fingerprint and DeadRecking. But this method have a problem that heap up the accident error when runinng in the long term, and estimate a greatly different location and location estimation accuracy worsens. In this paper, We proposed the method,leifd (Location Estimation method that Integrates Fingerprint and Dead reckoning) that treats Fingerprint and DeadReckoning evaluation in normalizing, and evaluated this method in experience by implementing prototype.in comparison to exsisting approach,leifd decreased average error distace 13% or more. Keywords: Indoor Localization Fingerprint DeadReckoning, 1. LAN LAN AP (Access Point) MN 1 Presently with Graduate School of Science and Technology, Keio University Fingerprint Fingerprint AP RSSI (Received Signal Strength Indicator) AP RSSI MN (Mobile Node) MN Fingerprint c 2014 Information Processing Society of Japan 1
2 Fingerprint Fingerprint AP AP RSSI LAN RSSI AP RSSI RSSI RSSI Fingerprint LEIFD (Location Estimation method that Integrates Fingerprint and Dead reckoning) Fingerprint 2 3 LEIFD TOA,TDOA,AOA LAN TOA (Time of Arrival) TDOA (Time Difference of Arrival) [1], AOA (Angle of Arrival) [2] TOA GPS AP MN AP MN TDOA AP MN MN AOA MN AP AP AP AP 2.2 MN AP RSSI RSSI Locky.jp [3],Loki [4] AP RSSI 2.3 Fingerprint Fingerprint[5][6][7][8] AP RSSI RSSI RSSI RSSI. 2.4 [9][10][11] MN MN MN D θ ˆl : (ˆx, ŷ) (ˆx, ŷ) = (x + D cos θ, y + D sin θ) (1) (x,y) MN, 2.5 [12] LAN RSSI MN 1 p 0 c 2014 Information Processing Society of Japan 2
3 p t (x, y) p 0 (x, y) Movement update RSSI update Step length and moving direction RSSI measurements 距離の確率分布 方向の確率分布 1 2 p(l t M t, R t 1 ) p(l t 1 M t 1, R t 1 ) p(l t 1 M t 1, R t 1 ) t 1 L t 1 M t 1 R t 1 RSSI p(l t M t, R t 1 ) φ φ A A p(l t M t, R t 1 ) = l p(l t L t 1 = l, D t, Φ t ) p(l t 1 = l M t 1, R t 1 ) (2) 3 D t Φ t t t L t L t 1 Start RSSI MN p(l t ) M t, R t 1 ) Monitoring acceleration sensor p(l t M t, R t ) = p(r t L t ) p(l t M t, R t 1 ) (3) r t t RSSI RSSI σ i RSSI p(r t L t ) : p(r t L t = l) = i 1 b 2πσi a e 1 2σ i 2 (r r i(l)) 2 dr (4) RSSI 1dB a = r t,i 0.5dB b = r t,i + 0.5dB MN 3 p(l t = l M t, R t ), No 4 Step detection Yes Coordinate update by Dead Reckoning MN walked n steps Yes Coordinate update by Fingerprint and Dead Reckoning LEIFD Fingerprint No c 2014 Information Processing Society of Japan 3
4 3.1 MN Fingerprint MN Fingerprint LAN LAN Fingerprint Fingerprint LEIFD Fingerprint Fingerprint LEIFD AP RSSI Fingerprint Fingerprint Fingerprint 3.2 Fingerprint Fingeprint AP RSSI 5 Fingerprint Fingerprint AP RSSI RSSI 5 MN-1 AP-A -60 RSSI RSSI AP :MN-1,MN-2 RSSI RSSI-A RSSI-B LEIFD Fingerprint Wall MN1 AP-A RSSI-A:-50dB RSSI-B:-60dB 5 Fingerprint 3.3 AP-B Wall MN2 RSSI-A:-50dB RSSI-B:-60dB t = 1 t = 1 t = 2 Fingerprint LEIFD Fingerprint LEIFD l i ˆl = argmaxli F in(l i ) + DR(l i ) (5) F in(i) i Fingerprint DR(i) i, 5 Fingerprint Fingerprint F in(i) DR(i) Fingerprint F in d (l i ) DR d (l i ) c 2014 Information Processing Society of Japan 4
5 F in d (l i ) = N ( r j,li r j ) 2 (6) DR d (l i ) = j=1 (DR x l ix ) 2 + (DR y l iy ) 2 (7) r j,li APj RSSI r j APj RSSI DR x DR y x y l ix l iy l i x y Fingerprint F in(l i ) = F in d(l i ) F in mind F in maxd F in mind + 1 (8) DR(l i ) = DR d(l i ) DR mind DR maxd DR mind + 1 (9) F in mind F in maxd Fingerprint DR mind DR maxd 8 9 Fingerprint DR 5 LEIFD Fingerprint 1 > < ( 1 ) α > β, Fingerprint RSSI RSSI Fingerprint ( 2 ) α < β 1:1 Fingerprint LEIFD Fingerprint AP MAC RSSI RSSI 4 LEIFD MN MN MN MN AP RSSI Fingerprint 8 9 Fingerprint 5 MN MN 4. 3 LEIFD Android Android: TOSHIBA REGZA Tablet AT500 Wi-Fi Android OS 4.0 Java RSSI SSID 6 MN RSSI RSSI Fingerprint LEIFD, RSSI LEIFD 3 LEIFD MN MN Fingerprint LEIFD 6 GUI c 2014 Information Processing Society of Japan 5
6 7 4.1 LEIFD AP 10 AP AP 20 Fingerprint LEIFD 1 6 MN 1 AP 3, 5, 10, i 45 D 60cm 20 Fingerprinting LEIFD 3 90m Android AP Android Android Android Android AP SSID RSSI MAC c 2014 Information Processing Society of Japan 6
7 20 RSSI 7 MN ( 1 ) Fingerprint MN MN 1 MN 10 MN Fingerprint MN 6 MN ( 2 ) MN p(l t 1 M t 1, R t 1 ) MN RSSI MN p(l t M t, R t ) 2 3 MN p(l t M t, R t ) MN ( 3 ) LEIFD MN MN 1 MN 10 MN Fingerprint Fingerprint F in(l i ) DR(l i ) 8 9 MN 5 MN LEIFD MN 6m LEIFD Fingerprint MN RSSI AP 6m AP RSSI RSSI RSSI ( AP 5 ) 20 RSSI AP RSSI 9 LEIFD 90 LEIFD AP 6m c 2014 Information Processing Society of Japan 7
8 5. Fingerprint LEIFD Fingerprint MN AP MAC RSSI MN MN MN RSSI Fingeprint Fingerprint MN RSSI MN Fingerprint Fingerprint RSSI AP LEIFD AP 5m 6m 13% [8] Shum, K., Cheng, Q. J., Ng, J. and Ng, D.: A Signal Strength Based Location Estimation Algorithm within a Wireless Network, IEEE International Conference on Advanced Information Networking and Applications (AINA), pp (online), DOI: /AINA (2011). [9] Beauregard, S. and Haas, H.: Pedestrian dead reckoning: A basis for personal positioning, Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (WPNC), pp (2006). [10] Pratama, A., Widyawan and Hidayat, R.: Smartphonebased Pedestrian Dead Reckoning as an indoor positioning system, International Conference on System Engineering and Technology (ICSET), pp. 1 6 (online), DOI: /ICSEngT (2012). [11] Gusenbauer, D., Isert, C. and Krosche, J.: Selfcontained indoor positioning on off-the-shelf mobile devices, International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1 9 (online), DOI: /IPIN (2010). [12] Seitz, J., Vaupel, T., Jahn, J., Meyer, S., Boronat, J. and Thielecke, J.: A Hidden Markov Model for urban navigation based on fingerprinting and pedestrian dead reckoning, 13th Conference on Information Fusion (FU- SION), pp. 1 8 (2010). [1] Liu, J., Cui, Q., Tao, X. and Zhang, P.: A Method to Enhance the Accuracy of Location Systems Based on TOA- Location Algorithms, 6th International Conference on ITS Telecommunications Proceedings, pp (online), DOI: /ITST (2006). [2] Shan, Z. and Yum, T.-S.: Precise Localization with Smart Antennas in Ad-Hoc Networks, IEEE International Conference on Global Telecommunications (GLOBECOM), pp (online), DOI: /GLOCOM (2007). [3] Locky.jp: (8 Janualy 2014). [4] Loki: (8 Janualy 2014). [5] Youssef, M. and Agrawala, A.: The Horus location determination system, Wireless Networks, Vol. 14, No. 3, pp (2008). [6] Cypriani, M., Lassabe, F., Canalda, P. and Spies, F.: Open Wireless Positioning System: A Wi-Fi-Based Indoor Positioning System, 70th IEEE International Conference on Vehicular Technology Conference (VTC), IEEE, pp. 1 5 (2009). [7] Leppakoski, H., Tikkinen, S. and Takala, J.: Optimizing radio map for WLAN fingerprinting, International Conference on Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), pp. 1 8 (online), DOI: /UPINLBS (2010). c 2014 Information Processing Society of Japan 8
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