Design protocol and performance analysis of indoor fingerprinting positioning systems

Moghtadaiee, Vahideh; Dempster, Andrew G.

doi:10.1016/j.phycom.2014.02.004

Cited by 17 publications

(11 citation statements)

References 29 publications

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“…Moghtadaiee [32] analyzed and modeled the number of APs and RPs. In their experiment, they used the root mean square (RMS) deviation of various positioning algorithms (like KNN and ANN) and the lower bounds of the non-Bayesian probability method with different numbers of APs and RPs and found that in an indoor space with dimensions of 11 × 23 m, the RMS of different methods can be as low as 5 APs and increasing their number barely influences the result.…”

Section: (3) Effects Of the Number Of Aps And Rpsmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

194

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: (3) Effects Of the Number Of Aps And Rpsmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

194

125

View full text Add to dashboard Cite

show abstract

“…On the other hand, the disadvantages of the technology include high requirements for data support and algorithm quality to ensure accuracy. Admittedly, there are two types of solutions including increasing collecting density of RPs and improving the algorithm to find better matching function between locations of RPs and their respective RSSI vectors [23]. From the aspect of practice, the former solution will increase costs which come from setting more access points (APs) in advance, taking more time to collect the fingerprinting data and processing more complex information.…”

Section: Wi-fi Fingerprinting Localizationmentioning

confidence: 99%

Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Zhang

Wang

Chen

et al. 2019

Sensors

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This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

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“…In addition, the effect of device diversity, which is commonly encountered in Wi-Fi, is not addressed in [15]. A positioning scheme which aims at optimizing indoor fingerprinting systems is proposed in [17]. According to this scheme, indoor fingerprinting systems can be optimized based on the systems operating parameters such as the number of access points (Aps), the geometry of APs, arrangement of RPs, and the number of RSS samples.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Bitew

Hsiao

Lin

et al. 2015

International Journal of Distributed Sensor Networks

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Indoor localization is used in many applications like security, healthcare, location based services, and social networking. Fingerprinting-based methods are widely used for indoor localization. But received signal strength (RSS) variation due to device diversity and change of conditions in the localization environment (e.g., distribution of furniture, people presence and movement, and opening and closing of doors) induce a significant localization error. To overcome this, we propose a hybrid indoor localization system using radio frequency (RF) and pyroelectric infrared (PIR) sensors. Our localization system has two stages. In the first stage, the zone of the target person is identified by PIR sensors. In the second stage, we apply K-nearest neighbor (K-NN) algorithm to the fingerprints within the zone identified and estimate position. Zone based processing of fingerprints will exclude deviated fingerprints because of RSS variation. We proposed two localization methods: Proposed 1 and Proposed 2 which use signal strength difference (SSD) and RSS, respectively. Simulation results show that the 0.8-meter accuracy of Proposed 1 achieves 84% and Proposed 2 achieves 65%, while traditional fingerprinting and SSD are 46% and 28%, respectively.

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Design protocol and performance analysis of indoor fingerprinting positioning systems

Cited by 17 publications

References 29 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

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