Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network

In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively.

[1]  Laurence T. Yang,et al.  Deep Computation Model for Unsupervised Feature Learning on Big Data , 2016, IEEE Transactions on Services Computing.

[2]  Sisi Zlatanova,et al.  Position, Location, Place and Area: AN Indoor Perspective , 2016 .

[3]  G. Sohn,et al.  INDOOR LOCALIZATION USING WI-FI BASED FINGERPRINTING AND TRILATERATION TECHIQUES FOR LBS APPLICATIONS , 2012 .

[4]  Hao Chen,et al.  ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information , 2017, IEEE Access.

[5]  Jason Jianjun Gu,et al.  Deep Neural Networks for wireless localization in indoor and outdoor environments , 2016, Neurocomputing.

[6]  C. Seelammal,et al.  On Data Cleaning with Intelligent Agents to Improve the Accuracy of Wi-Fi Positioning System using GIS , 2013 .

[7]  Chung-Hao Huang,et al.  Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation , 2015, IEEE Transactions on Instrumentation and Measurement.

[8]  Wenbo Zhang,et al.  Bayesian Filtering for Bluetooth RSS-based Indoor Tracking , 2016, ICCA 2016.

[9]  Adolfo Martínez Usó,et al.  UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[10]  Lei Yu,et al.  Fingerprinting localization based on neural networks and ultra-wideband signals , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[11]  Jian Wang,et al.  Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection , 2016, ISPRS Int. J. Geo Inf..

[12]  Seyed Ali Ghorashi,et al.  A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine , 2018, IEEE Sensors Letters.

[13]  Haiyong Luo,et al.  Indoor Positioning Based on Fingerprint-Image and Deep Learning , 2018, IEEE Access.

[14]  Ming Yan,et al.  Implementation of UWB indoor location and distance measurement based on TOF algorithm , 2018 .

[15]  Lei Gao,et al.  Statistical Machine Learning vs Deep Learning in Information Fusion: Competition or Collaboration? , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[16]  Seung-Hoon Hwang,et al.  Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning , 2019, Electronics.

[17]  Yu Zheng,et al.  Methodologies for Cross-Domain Data Fusion: An Overview , 2015, IEEE Transactions on Big Data.

[18]  Chuan Heng Foh,et al.  A practical path loss model for indoor WiFi positioning enhancement , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[19]  Dan Yang,et al.  Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm , 2018, Sensors.

[20]  Yuwei Chen,et al.  Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning , 2012, Sensors.

[21]  Junhai Luo,et al.  Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology , 2016, Int. J. Distributed Sens. Networks.

[22]  Marko Beko,et al.  On Target Localization Using Combined RSS and AoA Measurements , 2018, Sensors.

[23]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[24]  Jing Wang,et al.  Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning , 2017, PloS one.

[25]  Soon Ju Kang,et al.  A Situation-Aware Indoor Localization (SAIL) System Using a LF and RF Hybrid Approach , 2018, Sensors.

[26]  Ketan Rajawat,et al.  Dictionary-Based Statistical Fingerprinting for Indoor Localization , 2019, IEEE Transactions on Vehicular Technology.

[27]  Sudeep Pasricha,et al.  Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices , 2018, ACM Great Lakes Symposium on VLSI.

[28]  Federico Álvarez,et al.  SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking , 2019, IEEE Sensors Journal.

[29]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[30]  석용호,et al.  Method for channel sounding in wireless local area network and apparatus for the same , 2011 .

[31]  Laizhong Cui,et al.  A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network , 2018, Appl. Soft Comput..

[32]  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..

[33]  Peter A. Beerel,et al.  Morse Code Datasets for Machine Learning , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).