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Wireless LAN fingerprinting positioning technology is applied in indoor space equipped with some Wireless LAN access points. WLAN fingerprinting positioning technology utilize specified devices to receive WLAN signal and analyze signal strength in order to establish complete fingerprint database which experimental data can be loaded into to accomplish final positioning target under the instruction of specified pattern matching algorithm. Smart phone was developed into such a device to receive WLAN signal. After establishing fingerprint database, two pattern matching algorithms are used to achieve positioning target and verify the influence on positioning accuracy by WLAN signal direction and WLAN access point number. Introduction Currently indoor positioning technology based on WLAN signal is a major research focus within the scope of navigation and positioning technology which has the potential for enormous practical value. Current indoor positioning algorithms are mainly inherited from the traditional outdoor positioning technology, including TOA (Time of Arrival), AOA (Angle of Arrival), TDOA (Time Difference of Arrival) etc. Nowadays with the widespread popularity of IEEE802.11 protocol, positioning algorithms based on WLAN signal strength gradually play an important role in indoor positioning technology. Compared with the input parameter of traditional algorithms, such as the arrival time and arrival angle, the signal strength is more easily available and the accuracy can also be better assured. This advantage greatly enhances the operability of RSS (Received Signal Strength) positioning algorithm and laid solid foundation for RSS algorithm wide utilization in ordinary portable terminal devices, such as smart phones, tablet PCs and other wearable device. To implement RSS algorithm, experimenter should go through the following flow. Firstly experimenter divide space installed with several wireless networks AP (Access Point) into a scene positioning grids, each grid center called signal CP (Calibration Point). Then the signal strength is measured by the value of all APs consisting of WLAN signal strength vector at each CP by the portable device in order to establish a complete fingerprint database (Fingerprinting).Finally the signal strength is sampled at each TP from all APs and loaded into the above Fingerprinting database in order to obtain the final positioning estimated value utilizing specified pattern matching algorithm. This article is presented as the following order: pattern matching algorithm is introduced as chapter 1 and then experimental condition is introduced in chapter 2. In chapter 3, not only basic positioning result with the above pattern matching algorithm but also additional detailed study about influence on positioning precision from some chosen factors such as WLAN signal directional issue, Access Point Numbers and category are presented. Chapter 4 contains summary about all the contents in this article. Introduction of Pattern Matching Algorithm K-Nearest-Neighbor Algorithm. As the fundamental pattern classification algorithms, the core idea of K-Nearest-Neighbor algorithm is derived from Euclidean theorem, which calculates the distance between sample and all known patterns and estimate the sample as the known pattern with the International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME 2015) © 2015. The authors Published by Atlantis Press 987 shortest distance. Further, in order to improve the classification accuracy, the estimated value of sample is usually closest to the mean of K known patterns. In the calibration phase, each node receives the AP CP signal strength vector formula: ,1 , , , AP N z z z   =        (1) Among them, 1 CP N =   , i j z  Characterizes at position i , signal strength value from access point j ; AP N Characterizes the number of access points in the specified space; CP N Characterizes the number of calibration points in the specified space In the estimation stage, each CP signal strength vector from all AP values is shown by the following formula: 1, , AP N y y y   =     (2) Within the signal strength space, Euclidean distance between CP and AP is shown as following:

Study of WLAN Fingerprinting Indoor Positioning Technology based on Smart Phone