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Visible Light indoor positioning System Based on Pisarenko Harmonic Decomposition and Neural Network

Chinese Journal of Electronics 2024年第1期33卷 195-203页

作者： Li ZHAO Yi REN Qi WANG Lange DENG Feng ZHANG School of Electronic Information Engineering Xi’an Technological University

Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength(RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.

关键词： Visible-light communication indoor positioning Frequency estimation Pisarenko harmonic decomposition Back propagation neural network

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A Novel indoor positioning Framework

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1459-1477页

作者： Ming-Chih Chen Yin-Ting Cheng Ru-Wei Chen Department of Electronic Engineering National Kaohsiung University of Science and Technology(First Campus)Kaohsiung City82445Taiwan

Current positioning systems are primarily based on the Global positioning System(GPS).Although the GPS is accurate within 10 m,it is mainly used for outdoor positioning services(Location-Based Service;LBS).However,since satellite signals cannot penetrate buildings,indoor positioning has always been a blind spot for satellite signals.As indoor positioning applications are extensive with high commercial values,they have created a competitive niche in themarket.Existing indoor positioning technologies are unable to achieve less than 10 cmaccuracy except for the UltraWide Band(UWB)technology.On the other hand,the Bluetooth protocol achieves an accuracy of 1 to 2m.In this work,we use Bluetooth wireless signals to build a novel indoor positioning framework to avoid the high manufacturing costs involved in the UWB technology.The Bluetooth signals are combined with the results from artificial intelligence algorithms to improve accuracy.During laboratory indoor location tracking,the accuracy rate is 96%,which provides effective indoor tracking for the movement of people.

关键词： indoor positioning Bluetooth protocol artificial intelligence

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A Review of Device-Free indoor positioning for Home-Based Care of the Aged:Techniques and Technologies

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 1901-1940页

作者： Geng Chen Lili Cheng Rui Shao Qingbin Wang Shuihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obta

关键词： Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy

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Strong Tracking Particle Filter Based on the Chi-Square Test for indoor positioning

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1441-1455页

作者： Lingwu Qian Jianxiang Li Qi Tang Mengfei Liu Bingjie Yuan Guoli Ji Department of Automation Xiamen UniversityXiamen361102China Xiamen Tobacco Industry Co. Ltd.Xiamen361022China

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

关键词： NLOS strong tracking filter particle filter CST pedestrian dead reckoning indoor positioning

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Research Status and Trends of indoor positioning and Navigation Technology in China

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Journal of Geodesy and Geoinformation Science 2023年第3期6卷 87-101页

作者： Baoguo YU Lu HUANG Yachuan BAO Haonan JIA Shuang LI Chong CHEN Academy for Network&Communications of CETC Shijiazhuang 050081China State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang 050081China

As an essential component of future comprehensive positioning,Navigation,and Timing(PNT)system,indoor positioning technology has extensive application demands,making it a focal point of attention in both academia and industry.This article comprehensively reviews the research status of indoor positioning technology in China,with a focus on highlighting representative achievements and application validations from major research institutions in recent years.It addresses the challenges and issues faced in promotion and application of large-scale,high-precision indoor positioning.Furthermore,a universal and seamless indoor-outdoor positioning system architecture is proposed,along with a technical roadmap and key technologies to achieve this architecture.Finally,an analysis and outlook on future technological trends are presented.

关键词： indoor positioning location services integrated PNT

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indoor positioning System Using Axis Alignment and Complementary IMUs for Robot Localization

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机器人 2017年第3期39卷 316-323页

作者： ZHOU Qinqin LEI Siyu YU Zhangguo LIN Hsien-I CHEN Xuechao ZHANG Weimin Intelligent Robotics Institute Beijing Institute of Technology Graduate Institute of Automation Technology Taipei University of Technology

An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the accelerometer.The integral error of velocity is eliminated by a new subsection calculation method.Two complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement results.Secondly,an orientation estimation based on a fusion filter for the steering motion is proposed.Experiments show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.

关键词： axis alignment displacement estimation indoor positioning orientation estimation inertial measurement unit

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Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

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Defence Technology（防务技术） 2024年第3期33卷 573-587页

作者： Qu Wang Meixia Fu Jianquan Wang Lei Sun Rong Huang Xianda Li Zhuqing Jiang Yan Huang Changhui Jiang School of Automation Science and Electrical Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFo Shan528399China Research Institute of China Unicom Beijing100190China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China Department of Automation Tsinghua University Beijing100084China GEOLOC Laboratory Universite Gustave EiffelParis77454France

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

关键词： indoor positioning Inertial navigation system(INS) Zero-velocity update(ZUPT) Internet of things(IoTs) Location-based service(LBS)

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Accurate and efficient floor localization with scalable spiking graph neural networks

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Satellite Navigation 2024年第1期5卷 191-206页

作者： Fuqiang Gu Fangming Guo Fangwen Yu Xianlei Long Chao Chen Kai Liu Xuke Hu Jianga Shang Songtao Guo College of Computer Science Chongqing UniversityChongqingChina Department of Precision Instrument Tsinghua UniversityBeijingChina Institute of Data Science German Aerospace Center(DLR)JenaGermany School of Computer Science China University of GeosciencesWuhanChina

Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.

关键词： indoor positioning Deep learning Floor localization Spiking neural networks Graph neural networks

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An infant monitoring system with the support of accurate real-time indoor positioning

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Geo-Spatial Information Science 2019年第4期22卷 279-289,I0005页

作者： Zhipeng Cao Yifan Cheng Ruizhi Chen Guangyi Guo Feng Ye Liang Chen Yuanjin Pan State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina

This article presents an indoor infant monitoring system based on Android system which employs a low-cost Bluetooth Low Energy(BLE)pseudolite to accurately locate the infants in the activity indoors and timely remind the parents.It can achieve a positioning accuracy better than 2 m and effectively reduce the risk of injury to infants and young children.The performance of the proposed system,using false negative rate,false alarm rate,and alarm response time as indicators,is compared with that of the system using Quuppa as a positioning source.The experiment results show that both BLE pseudolite and Quuppa as a positioning source can basically meet the positioning accuracy for the infant monitoring system,but the BLE-pseudolite-based system provides a more cost-efficient solution.

关键词： indoor positioning Bluetooth Low Energy(BLE) pseudolite infant monitoring

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A regression model-based method for indoor positioning with compound location fingerprints

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Geo-Spatial Information Science 2019年第2期22卷 107-113,I0003页

作者： Tomofumi Takayama Takeshi Umezawa Nobuyoshi Komuro Noritaka Osawa Hewlett-Packard Japan LtdTokyoJapan Graduate School of Engineering Chiba UniversityChibaJapan

This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.

关键词： indoor positioning integrated estimation radio fingerprinting dead reckoning machine learning non-linear regression

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