Logistic regression-based device-free localization in changeable environments

Lei, Qian; Lv, Hengwei; Zhang, Haijian; Tang, Linling

doi:10.1109/icsp.2016.7877992

Cited by 2 publications

(2 citation statements)

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“…Other authors assume that the localization problem in scene analysis is a classification problem. Classical ML techniques such as KNN, SVM, DT, RF GP, logistical regression and support vector regression (SVR) and näive Bayes (NB), have been used in [55], [57], [60], [63]- [65], [72], [74], [76], [78], [80], and [81], to improve location accuracy by reducing the continuous problem into a discrete problem, in some cases as an area reduction to estimate the pedestrian localization with a simpler technique. For instance, the authors in [60] apply the DT to reduce the continuous localization area to obtain a small area that corresponds to the location of a pedestrian.…”

Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

“…Although it is more common in scene analysis for the existence of a device on the pedestrian to be localized, some researchers, such as in [43], [50], [68], [71], [75], [78], [79], are interested in device-free localization. An interesting proposal for device-free localization is presented in [75], in which the authors use RF to classify the error caused by environment changes and correct the fingerprint errors.…”

Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

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A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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