Dual-Band Wi-Fi Based Indoor Localization via Stacked Denosing Autoencoder

With the ever-increasing demand of location-based services (LBS), Wi-Fi based indoor localization has attracted increasing attentions. This paper is dedicated to addressing two critical problems: a) signal fluctuation due to unforeseeable interferences during the offline training phase; b) insufficient real-time signal measurements at certain point due to the target movement during the online localization phase. Specifically, we first give an intensive analysis on the characteristics of received signal strength indicator (RSSI) in indoor environments with respect to both time-domain and frequency-domain. Then, inspired from the advantages of Stacked Denosing Autoencoder (SDA) in terms of recognizing and stabilizing the original features, we propose a dual-band SDA (DBSDA) based model to create more distinguishable fingerprints by extracting the RSSI features at each reference point (RP). In this model, both 2.4GHz and 5GHz RSSIs are exploited to train the SDA neural network and construct the offline fingerprint database. On this basis, we propose a data generation scheme, which is designed based on the observation that environmental interferences are similar in proximate spots. So, the designed scheme can generate signal values at certain point based on its nearby RSSI measurements when there are not enough inputs for the SDA neural network. Finally, we propose a locally weighted liner regression (LWLR) based method to predict the coordinate of the target. For performance evaluation, we implement the system prototype and give comprehensive experiments in real-world environments, which demonstrate the effectiveness and robustness of the proposed solutions.

[1]  Yiran Peng,et al.  An Iterative Weighted KNN (IW-KNN) Based Indoor Localization Method in Bluetooth Low Energy (BLE) Environment , 2016, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld).

[2]  Byung Kook Kim,et al.  Dynamic Ultrasonic Hybrid Localization System for Indoor Mobile Robots , 2013, IEEE Transactions on Industrial Electronics.

[3]  Joseph Kee-Yin Ng,et al.  Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation , 2018, IEEE Transactions on Industrial Informatics.

[4]  Zhenzhong Chen,et al.  3-D BLE Indoor Localization Based on Denoising Autoencoder , 2017, IEEE Access.

[5]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[6]  Yan Wang,et al.  An Improved K-Nearest-Neighbor Indoor Localization Method Based on Spearman Distance , 2016, IEEE Signal Processing Letters.

[7]  Xiangyu Wang,et al.  CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi , 2017, 2017 IEEE International Conference on Communications (ICC).

[8]  Weiwei Wu,et al.  A Zero Site-Survey Overhead Indoor Tracking System using Particle Filter , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[9]  Jiming Chen,et al.  Gradient-Based Fingerprinting for Indoor Localization and Tracking , 2016, IEEE Transactions on Industrial Electronics.

[10]  Hao Jiang,et al.  Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization , 2015, Sensors.

[11]  Jianqiang Li,et al.  PSOTrack: A RFID-Based System for Random Moving Objects Tracking in Unconstrained Indoor Environment , 2018, IEEE Internet of Things Journal.

[12]  Naser El-Sheimy,et al.  Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons , 2016, Sensors.

[13]  Victor Lee,et al.  A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments , 2019, Neural Computing and Applications.

[14]  Pavel Pudil,et al.  Road sign classification using Laplace kernel classifier , 2000, Pattern Recognit. Lett..

[15]  Christian Wietfeld,et al.  Scalable and precise multi-UAV indoor navigation using TDOA-based UWB localization , 2017, 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN).