|Search by item||HOME > Access full text > Search by item|
JBE, vol. 23, no. 1, pp.154-161, January, 2018
Design and Implementation of Indoor Location Recognition System based on Fingerprint and Random Forest
Sunmin Lee and Nammee Moon
C.A E-mail: email@example.com
As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.
Keyword: Random Forest, Fingerprint, 실내위치인식, WiFi
 D. Han, and S. Jung, "global indoor location recognition and indoor / outdoor integrated navigation system," The Journal of The Korean Institute of Communication Sciences, Vol.32, No.2, pp. 89-97, January 2015.
 “Status of Wireless Communication Service Statistics,” Ministry of science and ICT, Aug 2017, http://msip.go.kr/SYNAP/skin/doc.html? fn=650ecd0147b4ea3d5adceac21064974f&rs=/SYNAP/sn3hcv/result/201801/
 J. Im, E. Lee, H. Kim, and K. Kim, “Image Grouping Technology based on Camera Sensors for Efficient Stitching of Multiple Images”, The Journal of Broadcast Engineering, Vol.22, No.6, Nov 2017.
 J. Kim, G. Jeong, Y. Hwang, P. Park, S. Park, and K. Kim, “Video Similarity Generating Algorithm Improving the Speed of Various Multi-Angle Image Composition”, Summer Conference of the Korean Society of Broad Engineers, Jeju, Korea, pp.399-402. June 2016.
 S. Park, “Trends in Indoor Location and Service Development,” Electronics and Telecommunications Research Institute, Vol.34, No.4, pp.3-9, April 2017.
 S. Park, Y. Cho, M. Ji, and J. Kim, “A Study on the trend of LBS technology and market,” Electronics and Telecommunications Research Institute, Dec 2015.
 M. Kim, B. Kim, Y. Ko, and K. Bang, “Indoor Location Tracking System of Low Energy Beacon using Gaussian Filter,” The Journal of Korean Institute of Information Technology, Vol.14, No.6, pp.67-74, 2016.
 C. Yoon, T. Kim, H. Kim, and Y. Hong, “Indoor Positioning Using RFID Technique,” Journal of the Korea Institute of Information and Communication Engineering, Vol.20, No.1, pp.207-214, 2016.
 S. Choi, H. Park, S. Lee, M. Son, Y. Koo, K. Park, and T. Kim, “An indoor location recognition scheme combining the triangulation method and fingerprinting,” Korean Institute of Information Scientists and Engineers, Vol.38, No.2, pp.112-114, 2011.
 T. Kim and D. Lee, “The Indoor Localization Algorithm using the Difference Means based on Fingerprint in Moving Wi-Fi Environment,” The Journal of Korean Institute of Communications and Information Sciences, Vol.41, No.11, pp.1463-1471, 2016.
 S. Son, Y. Park, B. Kim, and Y. Baek, “Wi-Fi Fingerprint Location Estimation System Based on Reliability,” The Journal of Korean Institute of Communications and Information Sciences, Vol.38, No.6, pp.531-539, 2013.
 J. Kim and N. Moon, “Multiple Object Tracking and Identification System Using CCTV and RFID,” Korea Information Processing Society (KIPS), Vol.6, No.2, pp.51-58, 2017.
 J. Jeong, K. Jang, and J. Kim, “Target Classification Method Using Random Forest and Genetic Algorithm,” Conference of the Proceeding of The Institue of Elec. and Info. Engineers, Daegu, Korea, pp.601-604, 2016.
 R. Malhotra, R. Jangra, "Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques," Journal of Information Processing System (JIPS), pp.778-804, Aug 2017.
 J. Choi, W. Ahn, and B. Seo, “An Efficient Classification of Digitally Modulated Signals Using Bandwidth Estimation,” Journal of broadcast engineering, pp.257-260, Vol.22, No.2, Mar 2017.