K-means-Jensen-Shannon divergence for a WLAN indoor positioning system

Abdullah, Osamah Ali; Abdel‐Qader, Ikhlas; Bazuin, Bradley J.

doi:10.1109/uemcon.2016.7777906

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…MDS is a set of statistical techniques that are used to visualize the information in order to find similarities/dissimilarities in the data. The matrix in MDS begins with item-item dissimilarities, and AP-AP distances are determined by a radio attenuation model [9]. The fingerprinting-based technique depends on matching algorithms (e.g., kNN) that have been used in RADAR [14], which is one of the first Wi-Fi signal strength-based IPS and is considered the basis of WLAN fingerprinting IPS.…”

Section: Related Workmentioning

confidence: 99%

“…The fingerprint-based technique is divided into offline and online phases. In the offline phase, the entire area of interest is divided into a rectangular set of grid points, and at each point, a site survey is taken by recording the RSS from APs, which is then stored in a database called the radio map [6][7][8][9][10]. In the online phase, the smartphone collects the RSS from the APs and sends it to the server to compare the predefined fingerprint of the offline phase with the RSS in the online phase in order to estimate the location on the grid map, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Algorithm for Wireless Indoor Localization

Abdullah¹,

Abdel‐Qader²

2018

Machine Learning - Advanced Techniques and Emerging Applications

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Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Wireless Indoor Localization

Abdullah¹,

Abdel‐Qader²

2018

Machine Learning - Advanced Techniques and Emerging Applications

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“…In recent years, while moving ahead with the smart-cities, the coverage area of WLAN network inside the building become more and more wide, therefore, using WLAN technology to achieve indoor positioning can avoid the purchase of special signal transmission and receiving equipment, it can lead to greatest reduction in cost [9]. Moreover, the accuracy of WLAN positioning can reach 1-10 meters by using appropriate positioning algorithm, which basically meets the needs of indoor positioning [10].…”

Section: Introductionmentioning

confidence: 99%

Learning to Improve WLAN Indoor Positioning Accuracy Based on DBSCAN-KRF Algorithm From RSS Fingerprint Data

Wang

Xiong

et al. 2019

IEEE Access

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WLAN-based indoor positioning algorithm has the characteristics of simple layout and low price, and it has gradually become a hotspot in both academia and industry. However, due to the poor stability of Wi-Fi signals, received signal strength (RSS) fingerprints of some adjacent reference positions are difficult to evaluate similarity when utilizing traditional distance-based calculation methods. By clustering these RSS fingerprints into one region, the commonly utilized KNN algorithm in the past cannot achieve accurate positioning in the region. For this, we introduce a concept of the insensitive region of the RSS fingerprint and a new algorithm named DBSCAN-KRF. This algorithm can delete noise sample and detect insensitive region, then, different methods are selected to achieve indoor positioning by judging the region of the estimated fingerprint sample, the KNN algorithm is selected when the region is sensitive, and random forest algorithm is selected when the region is insensitive. The experimental results show that the DBSCAN-KRF algorithm is superior while compared with other alternative indoor positioning algorithms. INDEX TERMS WLAN indoor position, control and optimization, machine learning, DBSCAN-KRF algorithm, fingerprint data.

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“…Hence, wireless localization technology based on RSS measurements still constitutes the mainstream research method for these types of applications [17]. Its positioning accuracy can reach 1-10 m [18]. More importantly, the proliferation of smart mobile devices facilitates the development of indoor localization based on WLAN.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Wang

Fan

et al. 2020

Mobile Information Systems

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Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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K-means-Jensen-Shannon divergence for a WLAN indoor positioning system

Cited by 10 publications

References 15 publications

Machine Learning Algorithm for Wireless Indoor Localization

Machine Learning Algorithm for Wireless Indoor Localization

Learning to Improve WLAN Indoor Positioning Accuracy Based on DBSCAN-KRF Algorithm From RSS Fingerprint Data

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

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