WiFi positioning algorithm in tunnel based on Fuzzy C-means clustering and KNN algorithm

WiFi fingerprinting positioning systems have been used in tunnels. But, the limitation and complexity of the tunnel environment, the solution to achieve a high accurate positioning system remains open. In order to solve this problem, we have done the following three tasks: 1) Different access point (AP) layout schemes are proposed for several different tunnel environments; 2) A dynamic fingerprint database based on test point signal is established; 3) An improved algorithm is designed by combining k-nearest neighbors(KNN) algorithm and fuzzy C-means clustering (FCM) algorithm. The experimental results show that the accuracy of WiFi positioning in tunnel is improved.

[1]  Ke Wang,et al.  Indoor infrared optical wireless localization system with background light power estimation capability. , 2017, Optics express.

[2]  Zheng Yao,et al.  A Feature-Scaling-Based $k$-Nearest Neighbor Algorithm for Indoor Positioning Systems , 2014, IEEE Internet of Things Journal.

[3]  David Cañete-Rebenaque,et al.  Hybrid Analog-Digital Processing System for Amplitude-Monopulse RSSI-Based MiMo WiFi Direction-of-Arrival Estimation , 2018, IEEE Journal of Selected Topics in Signal Processing.

[4]  Chih-Ning Huang,et al.  A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare , 2014, International journal of environmental research and public health.

[5]  Cheng Li,et al.  A Feature-Scaling-Based k-Nearest Neighbor Algorithm for Indoor Positioning Systems , 2016, IEEE Internet Things J..

[6]  Matteo Ridolfi,et al.  Experimental Evaluation of UWB Indoor Positioning for Sport Postures , 2018, Sensors.

[7]  Juha-Pekka Makela,et al.  Indoor geolocation science and technology , 2002, IEEE Commun. Mag..

[8]  Liangxiao Jiang,et al.  Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted , 2006, FSKD.

[9]  Santiago Mazuelas,et al.  Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks , 2009, IEEE Journal of Selected Topics in Signal Processing.

[10]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[11]  Jan Beutel,et al.  GPS-Equipped Wireless Sensor Network Node for High-Accuracy Positioning Applications , 2012, EWSN.

[12]  Dongkai Yang,et al.  WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest , 2018, Sensors.

[13]  Milos Borenovic,et al.  Space Partitioning Strategies for Indoor WLAN Positioning with Cascade-Connected ANN Structures , 2011, Int. J. Neural Syst..

[14]  Fuchun Sun,et al.  Robotic Room-Level Localization Using Multiple Sets of Sonar Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[15]  Jian Wang,et al.  A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System , 2015, Sensors.

[16]  K. J. Ray Liu,et al.  Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach , 2016, IEEE Internet of Things Journal.

[17]  Trung Dung Ngo,et al.  Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model , 2017, IEEE Transactions on Automation Science and Engineering.

[18]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[19]  Minyi Guo,et al.  Real-Time Locating Systems Using Active RFID for Internet of Things , 2016, IEEE Systems Journal.

[20]  Hyung Seok Kim,et al.  Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems , 2012, 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT).