A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor

Xu, Ming; Xia, Weiwei; Jia, Zhiwei; Zhu, Yaping; Shen, Lianfeng

doi:10.1109/vtcspring.2017.8108345

Cited by 16 publications

(12 citation statements)

References 9 publications

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“…To determine the position of mobile users within lighting ambiance, indoor positioning techniques have been improved for VLC systems by using machine learning methods [27][28][29][30]. Nguyen et al implemented a weighted K-nearest neighbor model on VLC systems to improve accuracy [28].…”

Section: Introductionmentioning

confidence: 99%

The modulation classification methods in PPM–VLC systems

Ağır

Sönmez

2023

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Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of PPM schemes in VLC systems by using Decision Tree, Knearest neighbor (KNN), Support Vector Machine (SVM), and a Direct Decision-based Linear Model (LM) technique. A novel feature extraction model is derived to be able to classify the type of PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The KNN method gives the best accuracy performance against other schemes at the SNR of 25dB and the distance of 2.32m and more, while the Tree model is superior to KNN at the distances of 2.25m and 2.20m. Additionally, it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20m.

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Section: Introductionmentioning

confidence: 99%

The modulation classification methods in PPM–VLC systems

Ağır

Sönmez

2023

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“…The proposed algorithm achieved an average positioning error of 3.20 cm with a positioning time cost of 0.36 s, thus making challenging to achieve real-time position [15]. Ming Xu et al combined the fingerprint positioning algorithm with the VLP algorithm, and use K-Nearest Neighbor (KNN) algorithm to achieve three-dimensional positioning [16]. The disadvantage of KNN is that the amount of calculation is large, because each sample to be located must calculate its distance to all known samples in order to obtain its K nearest neighbors [17].…”

Section: Introductionmentioning

confidence: 99%

Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine

Chen

Guan

et al. 2020

IEEE Access

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Photodiode-based (PD-based) visible light positioning (VLP) has become a research focus of indoor positioning technology, while the existing VLP models rarely consider the anti-interference and positioning time of that. In this paper, indoor real-time three-dimensional visible light positioning system using fingerprinting and extreme learning machine (ELM) is proposed to make the system achieve not only high positioning accuracy and elevated anti-interference but also well-behaved real-time ability. In contrast to the positioning system based on K-Nearest Neighbor or Support Vector Machine, the proposed system achieves the highest positioning accuracy and the state-of-the-art positioning speed. Furthermore, the visible light positioning kernel is proposed as a method to reduce the size of the fingerprint database and thus reduce the training time exponentially. Both the simulation and the experiment results show that the proposed system achieves real-time 3-D positioning with high anti-interference. Therefore, this scheme can be considered as one of the effective methods for indoor 3-D positioning. INDEX TERMS Extreme learning machine (ELM), photodiode (PD), positioning fingerprint, real-time positioning, visible light positioning (VLP).

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“…Using machine learning algorithms, many different approaches have been proposed with the overall goal of optimizing the location accuracy, complexity, and practical applicability of these techniques [22][23][24]. Among them, KNN is a preferred option [25][26][27][28]. The authors in [25] performed a field experiment for 3-D space based on RSS, fingerprint and KNN.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, KNN is a preferred option [25][26][27][28]. The authors in [25] performed a field experiment for 3-D space based on RSS, fingerprint and KNN. The experimental results proved that the above combination and weighted average method improved the positioning accuracy up to 0-6 cm.…”

Section: Introductionmentioning

confidence: 99%

High Precision Weighted Optimum K-Nearest Neighbors Algorithm for Indoor Visible Light Positioning Applications

Tran

2020

IEEE Access

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Enhancing the accuracy of indoor visible light positioning systems with simple, real-time, and stable methods is one of the interesting challenges in recent research. In this paper, a relatively minor mean positioning error of 8 mm and a 42-52% improvement in computational time could be achieved within a real space of 1.2 m x 1.2 m x 1.2 m by transcending the serious limitations of the traditional knearest neighbors (KNN) algorithm. These disadvantages (slow execution time, high error formation) are a result of finding the nearest neighbors from all the fingerprints, averaging the Euclidean distances, and the excessive passivity of the K value. To overcome the above limitations of KNN, we proposed a maximum received signal strength recognition (MRR) technique and weighted optimum KNN (WOKNN) algorithm, which is a combination of optimum KNN (OKNN) and weighted KNN (WKNN). While MRR was used to reduce the computational time, WOKNN was used to solve the remaining problems. Specifically, OKNN was used to automatically determine the best number of nearest neighbors for each position in the area under consideration, and WKNN helped improve the errors that come from the Euclidean distance averaging process. Based on positive experimental results and a meaningful comparison with various versions of KNN, we demonstrated that the improved conventional KNN algorithm can achieve very high positioning accuracy and is totally suitable for several specific 2-D indoor positioning applications.

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A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor

Cited by 16 publications

References 9 publications

The modulation classification methods in PPM–VLC systems

The modulation classification methods in PPM–VLC systems

Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine

High Precision Weighted Optimum K-Nearest Neighbors Algorithm for Indoor Visible Light Positioning Applications

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