A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Paterna, Vicente Cantón; Calveras, Anna; Paradells, Josep; Bullones, María Alejandra Pérez

doi:10.3390/s17122927

Cited by 169 publications

(102 citation statements)

References 34 publications

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“…It is interesting to compare our results to those reviewed in the literature. Reference [7] shows 1.82 m accuracy 90% of the time in a 6 by 9 m room and 4.6 m in a 16 by 17 m room, results which are close to ours (2.3 m in scenario #1 and 3.8 m in scenario #2, 90% of the time). Reference [6] achieved accuracies of less than 2.56 m 90% of the time, (an average of two trajectories) with a dense deployment of BLE beacons (one beacon per 9 m) but did not investigate obstacles.…”

Section: Discussionsupporting

confidence: 86%

“…In [6] the authors make use of the diversity of BLE channels in an algorithm combining a polynomial regression model, fingerprinting, two levels of outlier detection, and extended Kalman filtering, however, the investigated scenario was an empty corridor and the effect of obstacles was not investigated. Another recent study is presented in [7], where channel information, a Kalman filter and a trilateration method are applied to improve the precision of BLE RSSI-based trilateration. In this case, one or more mobile users were transmitting and several stationary receivers were deployed where each receiver was equipped with separate hardware to monitor each of the three advertising channels in parallel and then only the single channel with the least variation on each link was then used for trilateration.…”

Section: Introductionmentioning

confidence: 99%

“…A robust positioning system based on separate channels was investigated in [8]. Unlike [6][7][8] it begins by using training data to fit a path-loss model to each channel. Then in the operational stage, RSSI observations are taken and median values are used to estimate ranges for each channel.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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“…BLE RSS-based positioning methods are commonly affected by factors like the environment, beacon configuration, beacon deployment, and the receiving smartphone. Some of the methods found in literature address one or several of those factors [43]. To explore those factors in this work, two simple methods were tested: the weighted centroid method (applicable and used in BLE-based positioning because the beacon deployment positions are known) and the k-Nearest Neighbors (kNN) method commonly used in fingerprinting for indoor positioning.…”

Section: Positioning Accuracymentioning

confidence: 99%

BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

Mendoza-Silva

Matey-Sanz

Torres-Sospedra

et al. 2019

Data

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RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.

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“…Cantón Paterna et al [15] devised a solution to locate individuals within an environment. This solution accurately locates Bluetooth LE based devices, such as those placed on an at-risk individual, within an environment using multiple Bluetooth listeners.…”

Section: Related Workmentioning

confidence: 99%

Safe Beacon: A Bluetooth Based Solution to Monitor Egress of Dementia Sufferers within a Residential Setting

et al. 2018

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The global population is ageing, as a consequence of this there will be a greater incidence of ageing related illnesses which cause cognitive impairment–such as Alzheimer’s disease. Within residential care homes, such cognitive impairment can lead to wandering of individuals beyond the boundaries of safety provided. This wandering, particularly in urban areas can be life threatening. This study introduces a novel solution to detect, and alert caregivers of, egress of at-risk inhabitants of a care home. This solution operates through a combination of wearable Bluetooth beacons and beam-formed listening devices. In an evaluation process involving 275 egress events, this solution proved to offer accurate operation with no incidence of false positives. Notably, this solution has been deployed within a real residential care home environment for over 12 months. Proposed future work discusses improvements to this solution.

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A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Cited by 169 publications

References 34 publications

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

Safe Beacon: A Bluetooth Based Solution to Monitor Egress of Dementia Sufferers within a Residential Setting

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