Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Sensors

Chenxi Wu¹  Shiyi Xu²  Danshi Sun³  Erhu Wei³  Zhuoxi Ma⁴ 

[1] BGI Engineering Consultants Ltd., Beijing 100038, China;Beijing Satellite Navigation Center, Beijing 100094, China;School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;Xi’an Division of Surveying and Mapping, Xi’an 710054, China;

关键词: BLE sensors;    signal strength indicator;    indoor positioning;    bat algorithm;    convolutional neural network;   

DOI  :  10.3390/s21061995

来源: DOAJ

【 摘 要 】

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

【 授权许可】

Unknown