Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System

Zhang, Liye; Meng, Xin; Fang, Chonghua

doi:10.1155/2021/5530396

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Device heterogeneity is eliminated by applying a linear mapping between fingerprints from different devices [29]. Some of these methods are the Unsupervised Learning Algorithm [32], linear regression [33], and hyperbolic location fingerprinting (HLF).…”

Section: Device Diversitymentioning

confidence: 99%

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

Firdaus¹,

Fadhillah²,

Ahmad³

et al. 2023

IJE

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The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

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Section: Device Diversitymentioning

confidence: 99%

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

Firdaus¹,

Fadhillah²,

Ahmad³

et al. 2023

IJE

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“…Liye Zhang et al . used LiR in [33], with the maximum error reduced from 10m to 4.5m and the average error reduced from 3.72m to 2.31m.…”

Section: Related Workmentioning

confidence: 99%

Two-Phase Combined Model to Improve the Accuracy of Indoor Location Fingerprinting

Vu,

Ngo-Van,

Hoang Do Thanh

2022

JCC

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Wi-Fi Fingerprinting based Indoor Positioning System (IPS) aims to help locate and navigate users inside buildings. It has become a popular research topic in recent years. For the most parts, authors use the traditional machine learning algorithms to enhance the accuracy of locationing. Their methods involve using a standalone algorithm or a combination of different algorithms in only one phase, producing results with an acceptable accuracy. In this paper, we present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduction method. Specifically, our research is focused on the combination of different regression and classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extra Tree Regressor (extraTree), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR) and Linear Regression (LiR) to create a new data set and models that can be used in the training phase. These proposed models are tested on the UJIIndoorLoc 1 dataset. Our experimental results show a prediction accuracy of 98.73% by floor, and an estimated accuracy of 99.62% and 99.52% respectively by longitude and latitude. When compared with the results of the models in which we use independent algorithms, and of other researches that have different models using the same algorithms and on the same dataset, most of our results are better.

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Fast Construction of the Radio Map Based on the Improved Low‐Rank Matrix Completion and Recovery Method for an Indoor Positioning System

et al. 2021

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With the development of information technology, indoor positioning technology has been rapidly evolving. Due to the advantages of high positioning accuracy, low cost, and wide coverage simultaneously, received signal strength- (RSS-) based WLAN indoor positioning technology has become one of the mainstream technologies. A radio map is the basis for the realization of the WLAN positioning system. However, by reasons of the huge workload of RSS collection, the instability of wireless signal strength, and the disappearance of signals caused by the occlusion of people and objects, the construction of a radio map is time-consuming and inefficient. In order to rapidly deploy the WLAN indoor positioning system, an improved low-rank matrix completion method is proposed to construct the radio map. Firstly, we evenly arrange a small number of reference points (RP) in the positioning area and collect RSS data on the RP to construct the radio map. Then, the low-rank matrix completion method is used to fill a small amount of data in the radio map into a complete database. The Frobenius parameter (F-parameter) is introduced into the traditional low-rank matrix completion model to control the instability of the model solution when filling the data. To solve the noise problem caused by environment and equipment, a low-rank matrix recovery algorithm is used to eliminate noise. The experimental results show that the improved algorithm achieves the expected goal.

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Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System

Cited by 5 publications

References 34 publications

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

Two-Phase Combined Model to Improve the Accuracy of Indoor Location Fingerprinting

Fast Construction of the Radio Map Based on the Improved Low‐Rank Matrix Completion and Recovery Method for an Indoor Positioning System

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