Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

Chan, Yiu Wing Edwin; Soong, Boon‐Hee

doi:10.1109/access.2016.2612225

Cited by 17 publications

(3 citation statements)

References 32 publications

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“…Most ranging techniques require at least three known anchor nodes to calculate the location of the unknown target. Few range free techniques such as Centroid [10] method and DV hop [11] technique are also studied in the literature.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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“…However, owing to shadowing and multi‐pathing effects, modelling the UHF signal attenuation becomes difficult, resulting in inaccurate transmission distance measurement by the sensors [17]. Furthermore, a common method to determine the PD coordinates is the centroid method, which amplifies the measuring error caused by each sensor [18, 19]. Therefore, for RSSI‐based PD source localisation using signal propagation models, smoothing the RSSI signal, building an accurate ranging model, and finding a high precision positioning method are critical.…”

Section: Introductionmentioning

confidence: 99%

Intelligent learning approach for UHF partial discharge localisation in air‐insulated substations

et al. 2020

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To achieve comprehensive insulation deterioration motoring of power equipment and early fault warning in air‐insulated substations, a data‐driven partial discharge (PD) source localisation method employing noisy ultra‐high frequency (UHF) received signal strength indicator (RSSI) and particle filter is proposed in this study. Compared with the existing UHF time‐difference‐based techniques, UHF wireless sensor arrays and RSSI‐based methods provide an economical and high‐adaptability solution. However, owing to the multi‐pathing and shadowing effects, UHF signal attenuation cannot be modelled. Therefore, a Kalman filter was employed to smoothen the RSSI signal. Furthermore, a semi‐parametric regression model is proposed to achieve a more accurate relationship between the RSSI and the transmission distance. Finally, in contrast to traditional localisation algorithms directly based on the RSSI ranging model, a particle filter was used to achieve higher accuracy. It predicted the best distribution of the position of PD by learning and considering all the system states of the previous moment. The laboratory test was performed within an area of 6 m × 6 m, and the results demonstrate that the mean PD source localisation error was 1.16 m, which gives a potential application for the identification of power equipment with insulation deterioration in a substation, while the accuracy is still needed to be verified further by field tests.

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“…The pairs are used together to characterize the performance of dWCL in the form of the error distribution. The major content of this chapter is based on the works listed in [149,150].…”

Section: Discussionmentioning

confidence: 99%

Performance of positioning and lighting services for smart buildings : a wireless sensor network approach

Chan¹

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Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

Cited by 17 publications

References 32 publications

A Survey of Machine Learning for Indoor Positioning

A Survey of Machine Learning for Indoor Positioning

Intelligent learning approach for UHF partial discharge localisation in air‐insulated substations

Performance of positioning and lighting services for smart buildings : a wireless sensor network approach

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