Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Abstract

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

A survey of deep learning approaches for WiFi-based indoor positioning

“…Though it is not common for indoor positioning systems to achieve sub-metre level accuracy only using WiFi RSS, certain systems could still get very astonishing results by carefully modified models, for example Belmonte-Hernández et al (2019), Own et al (2019), G. Zhang et al (2019, Xue et al (2020), Hoang et al (2019, Soro and Lee (2019) and D. V. Nguyen et al (2018). The results of these systems are summarised in Table 3.…”

Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

“…The Automatic Relevance Determination based Matern Kernel 3/2 (ARD-MK 32) is a unique kernel function with two special hyperparameters, and it is given by (Zhang et al 2019) in (12):…”

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

“…Many technical solutions are found to be used for indoor localization, which can be classified into five categories: (1) wireless communication, (2) optical/visual, (3) acoustic, (4) electromagnetic, and (5) inertial measurement [9,17]. Despite some technical solutions being competitive in terms of accuracy, such as ultrasound and optical/visual techniques, they still present problems in their adoption due to the cost of additional equipment and difficulties in the deployment and maintenance of dedicated infrastructure [18].…”