K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona

doi:10.1109/glocomw.2015.7414026

Cited by 68 publications

(50 citation statements)

References 25 publications

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“…The weighted centroid algorithm described in [18]: This is one of the lowest complexity algorithms for estimating the indoor position, and it relies only on the estimated AP positions. The position estimate is the weighted centroid of the AP positions, where the weights are derived from the observed RSSs.…”

Section: Benchmark Indoor Positioning Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

143

104

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Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.

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Section: Benchmark Indoor Positioning Resultsmentioning

confidence: 99%

“…• shows examples of power maps in 2D and 3D views • shows examples of position estimation via two basic algorithms: weighted centroid estimator [18,19] and the log-Gaussian likelihood estimator [19,20].…”

Section: Supporting Softwarementioning

confidence: 99%

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

143

104

View full text Add to dashboard Cite

show abstract

“…The algorithm partitions a set of observations into k clusters and aims to minimize each withincluster sum-of-square. In [15], Razavi et al introduced a kmeans algorithm for fingerprint clustering in floor estimation. This method reduced the fingerprinting data size by storing and transmitting the cluster heads and related floor labels.…”

Section: Introductionmentioning

confidence: 99%

A tripartite filter design for seamless pedestrian navigation using recursive 2-means clustering and Tukey update

Peltola

Xiao

Hill

et al. 2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Abstract-Mobile devices are desired to guide users seamlessly to diverse destinations indoors and outdoors. The positioning fixing subsystems often provide poor quality measurements with gaps in an urban environment. No single position fixing technology works continuously. Many sensor fusion variations have been previously trialed to overcome this challenge, including the particle filter that is robust and the Kalman filter which is fast. However, a lack exists, of context aware, seamless systems that are able to use the most fit sensors and methods in the correct context. A novel adaptive and modular tripartite navigation filter design is presented to enable seamless navigation. It consists of a sensor subsystem, a context inference and a navigation filter blocks. A foot-mounted inertial measurement unit (IMU), a Global Navigation Satellite System (GNSS) receiver, Bluetooth Low Energy (BLE) and Ultrawideband (UWB) positioning systems were used in the evaluation implementation of this design. A novel recursive 2-means clustering method was developed to track multiple hypotheses when there are gaps in position fixes. The closest hypothesis to a new position fix is selected when the gap ends. Moreover, when the position fix quality measure is not reliable, a fusion approach using a Tukey-style particle filter measurement update is introduced. Results show the successful operation of the design implementation. The Tukey update improves accuracy by 5% and together with the clustering method the system robustness is enhanced.

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“…When the accuracy demand is high but the positioning area is huge, the database may be so large that the computational complexity of the matching algorithm is very high, which will bring more resource consumption and high power dissipation. Therefore, in order to reduce the computational complexity of the whole system, clustering methods are always necessary in the off-line training stage [18,19]. …”

Section: Introductionmentioning

confidence: 99%

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Liu

Deng

2016

Sensors

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Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

show abstract

K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Cited by 68 publications

References 25 publications

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

A tripartite filter design for seamless pedestrian navigation using recursive 2-means clustering and Tukey update

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

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