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An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Edgar Scavino1,*, Mohd Amiruddin Abd Rahman1, Zahid Farid2
1 Department of Physics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, 43400, Malaysia
2 Department of Electrical Engineering, Abasyn University, Khyber-Pakhtunkhwa, Pakistan
* Corresponding Author: Edgar Scavino. Email:

Computers, Materials & Continua 2023, 74(1), 379-397. https://doi.org/10.32604/cmc.2023.023824

Received 24 September 2021; Accepted 22 June 2022; Issue published 22 September 2022

Abstract

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

Keywords

QPSO; indoor localization; fingerprinting; neural networks; WiFi; WSN

Cite This Article

E. Scavino, M. A. A. Rahman and Z. Farid, "An improved hybrid indoor positioning algorithm via qpso and mlp signal weighting," Computers, Materials & Continua, vol. 74, no.1, pp. 379–397, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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