A real-time fingerprint-based indoor positioning using deep learning and preceding states

Article


Nabati, M. and Ghorashi, S. 2023. A real-time fingerprint-based indoor positioning using deep learning and preceding states. Expert Systems with Applications. 213 (Art. 118889). https://doi.org/10.1016/j.eswa.2022.118889
AuthorsNabati, M. and Ghorashi, S.
Abstract

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase.

In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.

KeywordsFingerprint-based positioning; Wi-Fi; smartphone; machine learning; deep learning
JournalExpert Systems with Applications
Journal citation213 (Art. 118889)
ISSN0957-4174
Year2023
PublisherElsevier
Accepted author manuscript
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Repository staff only
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2022.118889
Publication dates
Online24 Sep 2022
Publication process dates
Accepted20 Sep 2022
Deposited06 Oct 2022
Copyright holder© 2022 The Authors
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