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Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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24
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Sensors, Volume 21, issue 16
Abstract
Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm's potential, a novel localization-and-tracking system is presented to estimate a target's arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.
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Kaltiokallio, O, Hostettler, R, Yiğitler, H & Valkama, M 2021, 'Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm', Sensors, vol. 21, no. 16, 5549. https://doi.org/10.3390/s21165549