Multi-Detector Deep Neural Network for High Accuracy Wi-Fi Fingerprint Positioning
| Autor: | Alexander I-Chi Lai, Chung-Yuan Chen, Ruey-Beei Wu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Pipeline (computing) Fingerprint (computing) Normalization (image processing) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Fingerprint recognition 01 natural sciences 010309 optics Robustness (computer science) 0103 physical sciences Scalability 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Wireless sensor network |
| Zdroj: | WiSNet |
| DOI: | 10.1109/wisnet51848.2021.9413791 |
| Popis: | 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 $30 m \times 12m$ 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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