Abstract
Estimating the indoor position of users in commercial buildings remains a significant challenge to date. Although the WiFi‐based indoor localization has been widely explored in many works by employing received signal strength (RSS) patterns as the features, they usually lead to inaccurate results as the RSS could be easily affected by the indoor environmental dynamics. Besides, existing methods are computationally intensive, which have a high time consumption that makes them unsuitable for real‐life applications. In order to deal with those issues, we propose to use standardizing waveform tendency (SWT) of RSS for indoor positioning. We show that the proposed SWT is robust to the noise generated by the dynamic environment. We further develop a novel smartphone indoor positioning system by integrating SWT and kernel extreme learning machine (KELM) algorithm. Extensive real‐world positioning experiments are conducted to demonstrate the superiority of our proposed model in terms of both positioning accuracy and robustness to environmental changes when comparing with state‐of‐the‐art baselines.
Highlights
Over the past two decades, with the increasing popularity of smart devices, the demand of Location-Based Services (LBSs) comes with fast-pace increasing, for instance, driving to a destination, tracking, and recording user’s movements [1, 2]
BYS, k-nearest neighbor (KNN), Extreme learning machine (ELM), and OS-ELM, are chosen to further compare their performance when standardizing waveform tendency (SWT) and received signal strength (RSS) are employed as fingerprint characteristics, respectively
It can be observed that the positioning performance of each algorithm with RSS fingerprint characteristics is not as good as that with SWT, indicating that our standardized waveform trend method is better than the original RSS fingerprint method
Summary
Over the past two decades, with the increasing popularity of smart devices (e.g., smartphones and tablet computers), the demand of Location-Based Services (LBSs) comes with fast-pace increasing, for instance, driving to a destination, tracking, and recording user’s movements [1, 2] These services are commonly performed outdoors through Global Positioning System (GPS) and its derived applications (APPs). The proposed IPS improves the traditional fingerprint-based positioning system by applying the standardizing waveform tendency (SWT) of RSS and kernel extreme learning machine (KELM). It is worth noting that SWT can be integrated into existing WiFi positioning schemes (2) We propose a kernel extreme learning machine based localization algorithm for indoor smartphone positioning system.
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