A fusion optimization algorithm of network element layout for indoor positioning

Yu, Xiaomin; Wang, Huiqiang; Lv, Hongwu; Liu, Xiu-bing; Wu, Jinqiu

doi:10.1186/s13638-019-1597-8

Cited by 3 publications

(1 citation statement)

References 23 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That makes it possible to study DoP with respect to the deployment of the base station [19][20][21]. Recent research has focussed on system deployment on the complex spatial area [22,23]. There are also studies simulating the optimal DoPs for different anchor layout shapes [24][25][26].…”

Section: Location Error ¼ Dop ⋅ Measurement Error: ð1þmentioning

confidence: 99%

Dilution of precision for time difference of arrival with station deployment

Zhang

Ding

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

A study is conducted with the aim to reveal the relationship between the performance of moving object tracking algorithms and tracking anchor (station) deployment. The dilution of precision (DoP) for the time difference of arrival (TDoA) technique with respect to anchor deployment is studied. Linear and non-linear estimators are used for TDoA algorithms. The research findings for the linear estimator indicate that the DoP attains a lower value when other anchors are scattered around a central anchor; for the non-linear estimator, the DoP is optimal when the anchors are scattered around the target tag. Experiments on both algorithms are conducted that target location precision related to anchor deployment in practical situations for tracking moving objects integrated with a Kalman filter in an ultra-wideband (UWB)-based real-time localization system. The work provides a guideline for deploying anchors in UWB-based tracking systems.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Location Error ¼ Dop ⋅ Measurement Error: ð1þmentioning

confidence: 99%

Dilution of precision for time difference of arrival with station deployment

Zhang

Ding

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

show abstract

The Design of LED Array for Single Anchor-Based Visible Light Positioning

Cao,

Zhuang,

Wang

et al. 2023

IEEE Sensors J.

View full text Add to dashboard Cite

Machine learning and deep learning methods for wireless network applications

Chen

Jia

Hwang

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

Wireless networks have been widely adopted and introduced in the areas of engineering, manufacturing, weather monitoring, transportation, etc., to collect data to improve the quality of decision making, but issues arise, such as large volumes of data, incomplete and incompatible data sets, and noise data that prevent from realizing the true value and exploiting their full potentials. Machine learning and deep learning methods have been used as powerful tools to perform feature detection/extraction and trend estimation/forecasting in wireless networks applications. Supervised machine learning methods, for example, neural network (NN) [1, 2], convolutional neural network (CNN) [3], and recurrent neural network (RNN) [4], can be exploited in the applications pertinent to prediction and classification, whereas unsupervised machine learning methods such as restricted Boltzmann machine (RBM), deep belief network (DBN), deep Boltzmann machine (DBM), auto-encoder (AE), and denoising auto-encoder (DAE) [5] can be utilized for data denoising and model generalization. Furthermore, the reinforcement learning methods, including generative adversarial networks (GANs) and deep Q-networks (DQNs) [6], are tools for generative networks and discriminative networks to optimize the contesting process in a zero-sum game framework. These well-developed methods can contribute substantially, to better improve predictions and classifications in the relevant applications, but there are some issues and limitations that require further attention from the research communities.Topics covered in this issue are categorized into the following six themes: (1) networking optimization, (2) unmanned aerial vehicle (UAV) applications, (3) wireless sensor networks, (4) network security, (5) mobile positioning, and (6) image-based applications. This special issue has collected a total of 24 published papers from the USA, Australia, China, India, Pakistan, South Korea, and Vietnam to ensure that high-quality papers with significant results are selected and published. The accepted papers categorized into the aforementioned eight themes are briefly introduced below. Networking optimizationFour papers on networking optimization are listed as follows: (1) "Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach, " by Zhao Chen and Xiaodong Wang from China and the USA [7]; (2)

show abstract

A fusion optimization algorithm of network element layout for indoor positioning

Cited by 3 publications

References 23 publications

Dilution of precision for time difference of arrival with station deployment

Dilution of precision for time difference of arrival with station deployment

The Design of LED Array for Single Anchor-Based Visible Light Positioning

Machine learning and deep learning methods for wireless network applications

Contact Info

Product

Resources

About