A modified probability neural network indoor positioning technique

Chen, Chih-Yung; Yin, Li; Chen, Yu‐Ju; Hwang, Rey-Chue

doi:10.1109/isic.2012.6449770

Cited by 11 publications

(15 citation statements)

References 10 publications

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“…In the performing stage, mobile stations can detect the RSSIs of neighbor base stations and Wi-Fi APs, which can be adopted in the trained models to estimate the locations of these mobile stations. of estimated locations by the method were higher than those by the triangulation technique [11]. An improved neural network was trained with the correlation of the initial parameters to achieve the highest possible accuracy of the Wi-Fi-based positioning method in indoor environments [8].…”

Section: Related Workmentioning

confidence: 99%

“…For LBS in indoor environments, Wi-Fi-based positioning methods are popular techniques to detect and analyze the received signal strength indications (RSSIs) from Wi-Fi access points (APs) [5,8,[10][11][12][13][14][15][16][18][19][20][21]. The fingerprinting positioning methods based on machine learning algorithms were proposed to learn the relationships among locations and RSSIs for the estimation of locations.…”

Section: Introductionmentioning

confidence: 99%

“…Some deep learning methods (e.g., neural networks, convolutional neural networks, recurrent neural networks) have been applied to improve the accuracies of estimation locations [8,[11][12][13][14][15]].…”

Section: Introductionmentioning

confidence: 99%

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A Mobile Positioning Method Based on Deep Learning Techniques

Chen

Qishan

2019

Electronics

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This study proposes a mobile positioning method that adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., two timestamps and three timestamps); from the experimental results, it can be observed that the average error of location estimation was 9.19 m by the proposed mobile positioning method with two timestamps.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Mobile Positioning Method Based on Deep Learning Techniques

Chen

Qishan

2019

Electronics

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“…The authors in [21] pioneered the use of the probabilistic distribution measurement in IPS and proposed a probabilistic framework by using the Bayesian network to estimate the location. In [22] the authors used a modified probability neural network (MPNN) to estimate the coordinates of the object and found that it outperformed the triangulation method. In [23], a kernel method was proposed to estimate the object's location using a histogram of the RSSI at the unknown location.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithm for Wireless Indoor Localization

Abdullah¹,

Abdel‐Qader²

2018

Machine Learning - Advanced Techniques and Emerging Applications

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Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.

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“…Thus, the appropriate NN structure ensures more adequate interpretation of measurements. Chen, Yin, Chen, and Hwang present an indoor localization system with a Modified Probabilistic Neural Network operating in ZigBee network [15]. The system is designed to estimate an object's position with distorted RSS.…”

Section: Survey Of Related Workmentioning

confidence: 99%

Performance improvement of NN based RTLS by customization of NN structure - heuristic approach

Jachimczyk

Dziak

Kulesza

2015

2015 9th International Conference on Sensing Technology (ICST)

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The purpose of this research is to improve performance of the Hybrid Scene Analysis -Neural Network indoor localization algorithm applied in Real-time Locating System, RTLS. A properly customized structure of Neural Network and training algorithms for specific operating environment will enhance the system's performance in terms of localization accuracy and precision. Due to nonlinearity and model complexity, a heuristic analysis is suitable to evaluate NN performance for different environmental conditions. Efficiency of the proposed customization of a Neural Network is verified by simulations and validated by physical experiments. This research also concerns the influence of size of Neural Network training set. The results prove that, better localization accuracy is with a NN system which is properly customized with respect to a training method, number of neurons and type of transfer function in the hidden layer and also type of transfer function in the output layer.

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A modified probability neural network indoor positioning technique

Cited by 11 publications

References 10 publications

A Mobile Positioning Method Based on Deep Learning Techniques

A Mobile Positioning Method Based on Deep Learning Techniques

Machine Learning Algorithm for Wireless Indoor Localization

Performance improvement of NN based RTLS by customization of NN structure - heuristic approach

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