Wi-Fi-Based Localization in Dynamic Indoor Environment Using a Dynamic Neural Network

Fahed, Djabri; Liu, Rongke

doi:10.7763/ijmlc.2013.v3.286

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…The research of device and neural network positioning algorithms will be more and more in the future. [20,21]…”

Section: Three-dimensional Positioningmentioning

confidence: 99%

Analysis of the technology and development status of indoor positioning

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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RFID technology is an automatic identification technology that can accurately read and write tags through the radio frequency signals emitted by its equipment. At present, this technology has been widely used in various fields. It also has advantages over other positioning technologies in indoor positioning. This article analyzes and compares eight mainstream indoor positioning technologies including RFID technology in terms of accuracy, anti-interference, etc. At the same time, it also analyzes and compares the algorithms applicable to RFID indoor positioning, and focuses on the analysis and use of neural network technology. Combined with RFID technology to achieve positioning algorithms with strong environmental adaptability.

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“…The research of device and neural network positioning algorithms will be more and more in the future. [20,21]…”

Section: Three-dimensional Positioningmentioning

confidence: 99%

Analysis of the technology and development status of indoor positioning

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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“…For different scenarios, approximation capabilities are tested for Generalized Regression Neural Network, GRNN, and Multilayer Perceptron, MLP. In [16] the different types of NNs are evaluated under various conditions. The authors determine the most reliable and accurate type of NN architecture among: Nonlinear Autoregressive Network with eXogenous, Feed Forward Time-Delay Neural Network and Layer-Recurrent Network.…”

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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“…The recorded RSS values through experiment are used to train a feed-forward type of neural network in [10], it is shown that the neural network-based localization method is better than the well-known weighted k-nearest neighbor (KNN) method in term of cumulative distribution function. Three types of dynamic neural network (DNN) which can reduce the impact of non-stationarity of RSS on localization performance are used to localize the wireless device in [11]. By using Gaussian filter to process RSS value and fuzzy clustering to determine the center of radial basis function (RBF), two RBF neural network localization methods are proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Localization With TOA Measurements Based on Error Learning and Matching

Zhang

et al. 2019

IEEE Access

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Due to the widespread application of location information, the neural network localization method with the advantage of high localization accuracy has received significant interests in recent years. In this paper, we present two new neural network localization methods with time-of-arrival (TOA) measurements. In order to deal with three types of error about TOA measurements such as measurement error, non-line of sight (NLOS) error, and synchronization error, the proposed methods contain an offline training stage and an online localization stage. In the offline stage, the artificial neural network (ANN) or radial basis function (RBF) neural network is utilized to train the range measurements with the output of range errors rather than the position of the mobile terminal (MT). Moreover, due to the unknown signal propagation condition whether it is the line of sight or NLOS propagation, the k-mean clustering algorithm is used to classifying the range errors into different clusters. In the online stage, the range errors are predicted and updated, and then, the linear least square algorithm with the adjusted range measurements is applied for the position estimate of MT. Comparing with the ANN or RBF neural network localization methods, the simulation results show that the proposed methods can effectively reduce the localization error, especially when the training sample is not adequate. In addition, they are insensitive to measurement error, synchronization error, and the distribution of NLOS error. Finally, the memory requirement and computational complexity about different algorithms are analyzed and compared. INDEX TERMSArtificial neural network (ANN), localization, linear least square (LLS), non-line of sight (NLOS), radial basis function (RBF), time of arrival (TOA).However, the absence of direct line of sight (LOS) propagation with satellite will lead to severe deterioration of the localization performance [1]. There are some important supplements to the satellite-based technique, such as Wireless Sensor Network (WSN), Wireless Cellular Network (WCN) and Wireless Local Area Network (WLAN) [2], [3] techniques are used to localize the MT, especially in indoor environments and some outdoor environments where the satellite signals are heavily attenuated or blocked. These supplemental techniques have common principle to localize the MT. By measuring some signal parameters between MT and Fixed

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Wi-Fi-Based Localization in Dynamic Indoor Environment Using a Dynamic Neural Network

Cited by 13 publications

References 14 publications

Analysis of the technology and development status of indoor positioning

Analysis of the technology and development status of indoor positioning

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

Neural Network Localization With TOA Measurements Based on Error Learning and Matching

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