Multi-Detector Deep Neural Network for High Accuracy Wi-Fi Fingerprint Positioning
Journal
2021 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2021
Pages
37-39
Date Issued
2021
Author(s)
Abstract
A Deep Neural Network (DNN)-based positioning algorithm with multi-detector architecture is proposed for high accuracy Wi-Fi fingerprint positioning. Our DNN-based approach fuses the scalability of classifiers and the precision of regressors. Moreover, a pre-processing pipeline of signal readings is added for characteristic grouping and intra-sample normalization to improve the robustness. The algorithm was trained and tested on a robotically surveyed indoor fingerprint dataset including 349 reference points and 191 effective Wi-Fi access points in a 30 m × 12m area. As a result, our algorithm is capable of positioning with 1.08 m mean distance error in a leave-10%-out test, performing nearly three times as good as the referenced WKNN baseline. ? 2021 IEEE.
Subjects
deep neural network; fingerprinting; indoor navigation; indoor positioning; Internet of Things; machine learning
Other Subjects
Deep neural networks; Palmprint recognition; Wi-Fi; Wireless local area networks (WLAN); Wireless sensor networks; Fingerprint dataset; High-accuracy; Mean distances; Multi-detectors; Positioning algorithms; Pre-processing; Reference points; Wi-fi access points; Neural networks
Type
conference paper