Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression

Sun, Wei; Xue, Min; Huang, Yu; Tang, Hongwei; Lin, Anping

doi:10.1109/tvt.2018.2870160

Cited by 148 publications

(79 citation statements)

References 40 publications

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“…In our experiment, the direct use of KNN, multilayer NN and SVM for flat classification-based object position estimation are selected as the benchmarks for performance evaluation. Specifically, the multilayer NN has 8 fully-connected hidden layers with 10,18,27,35,22,16,15 and 22 neurons, respectively, and they adopt the tanh activation function. The cross-entropy loss is employed in the training process, where 85% of the training data is used for optimizing the connection weights while the remaining 15% is applied for cross validation to avoid overfitting.…”

Section: Positioning Accuracymentioning

confidence: 99%

“…Firstly, Wi-Fi access points (APs) have been extensively deployed in indoor environments; secondly, measuring Wi-Fi RSS is readily available in the current Wi-Fi terminals. Many regression techniques have become available for Wi-Fi RSS indoor positioning and they include the distance-based [13,14] and Gaussian Processes (GP)-based techniques [15,16]. In fact, the use of the standard log-normal model for indoor positioning (see, e.g., [17]) can be considered as a regression technique as well.In this work, we shall take a different approach and consider the fingerprint-based indoor localization method.…”

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confidence: 99%

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Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Zhang

Qin

Xue

et al. 2020

Sensors

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Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

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Section: Positioning Accuracymentioning

confidence: 99%

mentioning

confidence: 99%

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Zhang

Qin

Xue

et al. 2020

Sensors

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show abstract

“…During transmission, electromagnetic wave signals may be subjected to multipath interference or obstruction. In this case, the signal strength received at the same position varies from time to time [16][17][18]. The reliability of the measured RSSIs should be enhanced before indoor positioning.…”

Section: Proposed Indoor Positioning Algorithmmentioning

confidence: 99%

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Ren¹,

Huang²,

Song³

et al. 2019

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Considering the high accuracy needed for indoor positioning, this paper develops a novel indoor positioning algorithm for the wireless sensor network (WSN) in the following steps. First, the RSSIs of the network nodes were sampled and analyzed, and the excess errors were filtered to enhance positioning accuracy. Next, the initial position was iteratively obtained by the weighted centroid algorithm, and a correction matrix was developed to improve the Taylor series expansion (TSE), and the final position was determined through improved TSE iteration. The proposed positioning method was verified through simulation.

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“…The actual performance of RF localisation is deeprooted in the technologies which are utilised to achieve it. Wi-Fi [42,144] has been cited as one of the more popular approaches. Increasingly, the Bluetooth Low Energy (BLE) based sensors have been used, which leverage the low-power consumption with cheap cost and ubiquity [13,150].…”

Section: Radio Frequency Sensorsmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression

Cited by 148 publications

References 40 publications

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Sensor Modalities and Fusion for Robust Indoor Localisation

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