An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

Chen, Lina; Li, Binghao; Zhao, Kai; Rizos, Chris; Zheng, Zhengqi

doi:10.3390/s130811085

Cited by 89 publications

(63 citation statements)

References 15 publications

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“…For every location on each frequent route, the algorithm assigns a decaying probability to all those fingerprints in the radiomap, which reside within a circle of radius ρ around this location. This decaying probability is assumed to follow a Gaussian Normal Distribution which is a traditional method used in fingerprint database generation [44], [45] given by:…”

Section: A Route Probability Factormentioning

confidence: 99%

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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Abstract-This paper studies the use of deterministic channel modelling through 3D Ray Tracing (RT) for constructing deviceindependent radiomaps for Wi-Fi RSSI-based fingerprinting indoor positioning, applicable to different devices. Device heterogeneity constitutes a limitation in fingerprint-based approaches and also constructing radiomaps through extensive in-situ measurement campaigns is laborious and time-consuming even with a single device let alone the need for radiomaps constructed using multiple different devices. This work tackles both challenges through the use of 3D RT for radiomap generation in conjunction with data calibration using a small set of device-specific measurements to make the radiomap device-independent. The efficiency of this approach is evaluated using simulations and measurements in terms of the time spent to generate the radiomap, the amount of device-specific data required for calibration and in terms of the achievable positioning accuracy. Potential accuracy improvements in the RT-based indoor positioning processes are further investigated, by studying the use of map constraints into the algorithm in the form of a-priori probabilities. In this approach, a Route Probability Factor (RPF), which reflects the likelihood of a user being in various locations inside the environment is used. The outcome of the evaluation process which includes a study of different RPF distributions, indicates the validity of the approach, demonstrated by a reduction in the positioning error for various devices. The versatility of this approach is also demonstrated for different scenarios, different devices and by considering different device-handling conditions.

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Section: A Route Probability Factormentioning

confidence: 99%

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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“…Horus system is based on the probabilistic approach which considers the statistical characteristics of the RSSIs and their distribution. The Lognormal distribution [10], Weibull function [5], Gaussian distribution [11], and the double peak 2 Mobile Information Systems Gaussian distribution [12] were used to model the RSSI distribution. However, RADAR system was the first introducing the fingerprinting technique based on the deterministic approach [13] using -Nearest Neighbor method (KNN) [14].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

Rezgui

Pei

Chen

et al. 2017

Mobile Information Systems

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This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

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“…Park put forward the organic construction scheme of fingerprint library, using user-based RSS acquisition to reduce the workload of collecting location fingerprints, and using clustering and Voronoi diagram to organize the development of this fingerprint library [2]. Chen L proposed an improved bimodal Gaussian distribution to reduce the workload of off-line data training [3]. Through the comparison of the existing methods, we can think of the method of constructing the fingerprint information base by using the signal propagation model, and calculate the fingerprint information by calculating the strength and distance of the AP node to construct the fingerprint information base.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint database construction method based on optimized attenuation factor model

Di¹,

Ma²,

Liang³

2017

2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017)

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Abstract. Aiming at the construction stage of off-line fingerprint database of fingerprint positioning technology based on WiFi, for the construction of traditional fingerprint database needs field acquisition of fingerprint signal widely, it is difficult to achieve in a large area, a method is proposed to construct the fingerprint database based on optimal attenuation factor model. Firstly, the traditional attenuation factor model was optimized, the free space model and the attenuation factor model were combined, and through the signal acquisition of several nodes, it realized the self-adaption of the path loss index in attenuation factor model. Secondly, the method of constructing the fingerprint database by using the optimized attenuation factor model was given. Finally, the optimization is confirmed by simulating with a variety of classical propagation model.

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An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

Cited by 89 publications

References 15 publications

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

Fingerprint database construction method based on optimized attenuation factor model

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