Hybrid Indoor Position Estimation using K-NN and MinMax

Subhan, Fazlı; Ahmed, Shakeel; Haider, Sajjad; Saleem, Sajid; Khan, Akhtar Naeem; Ahmed, S.; Numan, Muhammad

doi:10.3837/tiis.2019.09.005

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Some researches proposed to use Global Positioning System (GPS) [2,25,26] with IMU sensor to overcome the problem of a long-term drift. However, the major problem of the GPS is intermittent loss of signal, especially for indoor environment [3]. Vision-based tracking has high accuracy for pose estimation [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Pose Tracking of Moving Sensor using Monocular Camera and IMU Sensor

2021

KSII TIIS

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Section: Introductionmentioning

confidence: 99%

Pose Tracking of Moving Sensor using Monocular Camera and IMU Sensor

2021

KSII TIIS

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“…There are also some hybrid solutions that combine distance-based localization techniques with the fingerprinting techniques to improve the position estimation accuracy [6,7]. These methods may improve localization accuracy at one location, but due to fluctuations in transmission power, especially in the case of Bluetooth, accurate position estimation is still a challenging task [8]. In order to address this problem, we propose a method based on linear discriminant analysis (LDA) for the tracking and position estimation of a dynamic object in an indoor environment using the Bluetooth Low Energy modules.…”

Section: Introductionmentioning

confidence: 99%

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

et al. 2020

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Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.

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Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Pasha¹,

Umair²,

Mirza³

et al. 2021

Computers, Materials &Amp; Continua

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Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternative methods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for lineof-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m 2 area.

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Hybrid Indoor Position Estimation using K-NN and MinMax

Cited by 3 publications

References 25 publications

Pose Tracking of Moving Sensor using Monocular Camera and IMU Sensor

Pose Tracking of Moving Sensor using Monocular Camera and IMU Sensor

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

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