Novel calibration algorithm for received signal strength based indoor real-time locating systems

Narzullaev, Anvar; Park, Yongwan

doi:10.1016/j.aeue.2013.01.006

Cited by 21 publications

(17 citation statements)

References 13 publications

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“…Although the proposed method reduces the calibration time, the location accuracy of the model is a bit on the downside. In our previous study [22] we presented an interpolation based calibration algorithm that utilizes a modified one-slope model (MOSM). Instead of using fixed reference path-loss values, the proposed method requires a small number of reference samples collected in a target area.…”

Section: B Rssi Approximationmentioning

confidence: 99%

“…We compared the proposed method (CHEB) with conventional measurement based calibration algorithm (CONV), as well as, with two recent interpolation based methods. The first method is VORO described in [21], and the other one is MOSM presented in [22]. VORO requires two parameters, p denoting power to the weights and Q denoting the number of the closest RPs for the refinement.…”

Section: A Experiments Setupmentioning

confidence: 99%

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Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Narzullaev

Muminov

Ibragimov

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm.

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Section: B Rssi Approximationmentioning

confidence: 99%

Section: A Experiments Setupmentioning

confidence: 99%

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Narzullaev

Muminov

Ibragimov

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Recently, research in WiFi-based indoor localization explored its feasibility using smartphones with various techniques [11,17,18,19,20,21,22,23,24,25,26,27,28]. In choosing the Wi-Fi signals to be used to uniquely classify a location, different authors used different approaches in their methods.…”

Section: Related Workmentioning

confidence: 99%

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting

Husen

Lee

2016

Sensors

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A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time. The invariant RSS statistics thus collected serve as the reference pattern classes for fingerprinting. Fingerprinting is carried out at an unknown location by identifying the reference pattern class that maximally supports the spontaneous RSS sensed from individual Wi-Fi sources. A design guideline is also presented as a rule of thumb for estimating the number of Wi-Fi signal sources required to be available for any given number of calibration locations under a certain level of random spatiotemporal disturbances. Experimental results show that the proposed method not only provides 17% higher success rate than conventional ones but also removes the need for recalibration. Furthermore, the resolution is shown finer by 40% with the execution time more than an order of magnitude faster than the conventional methods. These results are also backed up by theoretical analysis.

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“…Generally, positioning problems could be classified as outdoor and indoor. The outdoor positioning mainly uses GPS (Global Positioning System), GPRS (General Packet Radio Service), and so forth, while the indoor positioning mostly utilizes short range signals, such as Wi-Fi (Wireless-Fidelity) [3,4] and Bluetooth [5][6][7]. And the testing position is determined based on the received signals.…”

Section: Introductionmentioning

confidence: 99%

IWKNN: An Effective Bluetooth Positioning Method Based on Isomap and WKNN

Wang

Feng

Zhang

et al. 2016

Mobile Information Systems

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Recently, Bluetooth-based indoor positioning has become a hot research topic. However, the instability of Bluetooth RSSI (Received Signal Strength Indicator) promotes a huge challenge in localization accuracy. To improve the localization accuracy, this paper measures the distance of RSSI vectors on their low-dimensional manifold and proposes a novel positioning method IWKNN (Isomap-based Weighted K-Nearest Neighbor). The proposed method firstly uses Isomap to generate low-dimensional embedding for RSSI vectors. Then, the distance of two given RSSI vectors is measured by Euclidean distance of their low-dimensional embeddings. Finally, the position is calculated by WKNN. Experiment indicates that the proposed approach is more robust and accurate.

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Novel calibration algorithm for received signal strength based indoor real-time locating systems

Cited by 21 publications

References 13 publications

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting

IWKNN: An Effective Bluetooth Positioning Method Based on Isomap and WKNN

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