Survey on WiFi‐based indoor positioning techniques

Fen, Liu; Liu, Jing; Yin, Yuqing; Wang, Wenhan; Dong-hai, HU; Chen, Pengpeng; Niu, Qiang

doi:10.1049/iet-com.2019.1059

Cited by 201 publications

(98 citation statements)

References 76 publications

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“…Wi-Fi: Wi-Fi is most widely used for IPS because of the ubiquitous availability of Wi-Fi systems [53]. It can provide fairly large coverage range however, the power consumption of WLAN systems are comparatively higher [54]. Typically, the Wi-Fi based localization methods are trilateration or fingerprint based.…”

Section: Wireless Technologies Used For Indoor Localizationmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

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Section: Wireless Technologies Used For Indoor Localizationmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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“…UWB technology usually implemented for its precise indoor positioning. This technology uses a nanosecond radio pulse to transmit data in a wide bandwidth ranges from greater than 500MHz [1]. Time of Arrival (TOA) and Time Difference of Arrival (TDOA) measures can be used together with this UWB technology to determine the distance between the target and reference point.…”

Section: Ultra-wide Band (Uwb)mentioning

confidence: 99%

“…Interference and noise from other device also plays an important role in degrading the accuracy of indoor positioning. Accuracy and precision of some 50 meters inside a commercial setting is useless with respect to a task such as locating specific merchandise on a shelf [1]. Thus, the need for specialized methods and technologies for indoor location systems has been widely accepted.…”

Section: Introductionmentioning

confidence: 99%

Indoor positioning: technology comparison analysis

Din¹,

Jamil²,

Maniam

et al. 2018

IJET

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A system that allows users to find and track a specific position is known as positioning system. Global Positioning System (GPS) is one of top known position tracking system that commonly used to find position and location of object outdoor. Tracking an object indoor using GPS is not highly recommended because the signals transmitted through a satellite to a device indoor gets blocked and resulted in weak signals. Thus, an indoor positioning system (IPS) that tracks and positions object indoor has been implemented to overcome the issues of signals multipath that resulted from GPS. The aim of this paper is to provide up to date indoor positioning technologies and compares the technologies according to its technical perspectives.

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“…From the academic perspective, fingerprint positioning algorithms can be divided into deterministic [ 4 ] and probabilistic methods [ 5 ]. Deterministic algorithms are easy to implement.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

Ruan

Ling

Zhou

et al. 2020

Sensors

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The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

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Survey on WiFi‐based indoor positioning techniques

Cited by 201 publications

References 76 publications

A Survey of Machine Learning for Indoor Positioning

A Survey of Machine Learning for Indoor Positioning

Indoor positioning: technology comparison analysis

An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

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