An Algorithm Based on Bayes Inference And K-nearest Neighbor For 3D WLAN Indoor Positioning

Nascimento, Hitalo Joseferson Batista; Rodrigues, Emanuel B.; Cavalcanti, Francisco R. P.; Paiva, Antonio

doi:10.14209/sbrt.2016.194

Cited by 5 publications

(4 citation statements)

References 6 publications

(9 reference statements)

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“…Depending on the algorithm used, the precision and complexity of the approach vary. Some of the methods to carry out the geolocation using WiFi networks are: power vector, power triangulation, proximity heuristics [6], method of the k-nearest neighbors [16], [17], probabilistic methods [15], and location using RTT through ping messages [1].…”

Section: ) Methods Of the K-nearest Neighborsmentioning

confidence: 99%

A Proposal for a Technological Solution to Improve user Experience in a Shopping Center based on Indoor Geolocation Services

Andrade¹,

Quintero²,

Gamess³

et al. 2018

ijacsa

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For shopping centers, mobile devices and their associated technologies represent great business opportunities and a way to improve the user experience within their facilities. These types of constructions are usually quite large, multi-story, and with a significant number of shops, services, where the visitors may find themselves having difficulties to have a complete and up-to-date list of the stores, determine which stores and services are those that meet the characteristics or specifications they seek, know the location of the shops or how to reach them. This research studies and contemplates different technologies, tools, and approaches for the development of a technological solution for shopping centers that offers in its functionality a geolocation system in the interior spaces of the buildings. Our technological solution includes a mobile application for the Android operating system implemented by using the native development approach, and a web application for managing data, where the contents and settings of the mobile application will be obtained following the client/server model through a private API. It is worth to mention that the already mentioned system of geolocation in interiors is implemented using WiFi technology and the different Access Points installed in the shopping center, through which users can obtain their position, locate the stores or services of their interest, and receive indications on how to reach them.

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Section: ) Methods Of the K-nearest Neighborsmentioning

confidence: 99%

A Proposal for a Technological Solution to Improve user Experience in a Shopping Center based on Indoor Geolocation Services

Andrade¹,

Quintero²,

Gamess³

et al. 2018

ijacsa

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show abstract

“…However, this system utilizes numerous RPs to reduce localization error. Nascimento et al 24 proposed a localization system based on the RF fingerprinting technique. This system used Bayes inference to locate a target in 3D indoor environments.…”

Section: Related Workmentioning

confidence: 99%

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

Alhammadi¹,

Hashim²,

Rasid³

et al. 2020

International Journal of Distributed Sensor Networks

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Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

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“…Nascimento Hitalo J.B. et al proposed a positioning algorithm [ 10 ] based on Bayes inference to locate objects in 3D WLAN indoor environments. This is a fingerprint technique and the average positioning error is about three meters.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the WiFi indoor positioning algorithm [ 10 ] and the KNN algorithm [ 26 ], and inspired by Sun Y. and Gu F. who use the sparse signal to descript all the information of the original signal with high probability in the literature [ 44 ], we propose an RFID indoor positioning algorithm based on Bayesian probability and K -Nearest Neighbor, using a Gaussian filter to filter the abnormal Received Signal Strength (RSS) value and using the proper k value and the Bayesian estimation method to improve the accuracy of the location. Rather than WiFi technology [ 10 ] and active RFID technology [ 25 ], we use passive UHF RFID technology to perform indoor positioning resulting in a lower location error of about 15 cm. When comparing the proposed algorithm with the traditional LANDMARC algorithm and other related algorithms, the results show that our algorithm performs with a higher location accuracy.…”

Section: Introductionmentioning

confidence: 99%

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

Ding

et al. 2017

Sensors

145

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The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

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An Algorithm Based on Bayes Inference And K-nearest Neighbor For 3D WLAN Indoor Positioning

Cited by 5 publications

References 6 publications

A Proposal for a Technological Solution to Improve user Experience in a Shopping Center based on Indoor Geolocation Services

A Proposal for a Technological Solution to Improve user Experience in a Shopping Center based on Indoor Geolocation Services

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor

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