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Survey of indoor localization Based on Deep Learning

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Computers, Materials & Continua 2024年第5期79卷 3261-3298页

作者： Khaldon Azzam Kordi Mardeni Roslee Mohamad Yusoff Alias Abdulraqeb Alhammadi Athar Waseem Anwar Faizd Osman Centre for Wireless Technology Faculty of EngineeringMultimedia UniversityCyberjaya63100Malaysia Communication Systems&Networks Research Lab Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Electrical Engineering(DEE) International Islamic UniversityIslamabad44000Pakistan Rohde&Schwarz(M)Sdn Bhd Shah Alam40150Malaysia

This study comprehensively examines the current state of deep learning (DL) usage in indoor *** emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing *** deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

关键词： Deep learning indoor localization wireless-based localization

Improved PSO-Extreme Learning Machine Algorithm for indoor localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua School of Information Engineering East China Jiaotong UniversityNanchang 330013China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related *** basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location ***,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy *** this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting ***,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it ***,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are ***,extensive simulation results are *** is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

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Journal of Electronic Science and Technology 2024年第1期22卷 42-52页

作者： Yan Liang Song Chen Xin Dong Tu Liu School of Computer Engineering Chengdu Technological UniversityChengdu611730China HAN Networks Corporation Limited Beijing100094China

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor ***,the construction of the fingerprint database is quite *** when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is *** appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)*** this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization *** points(RPs)are generated in the grid by the FGGC ***,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex *** a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous *** proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy *** mean error of the proposed model is 0.36 m,far lower than that of previous ***,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor *** also shows high-accuracy performance with a fast running speed even under a large-size *** results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

关键词： Fine-grained grid computing (FGGC) indoor localization Path loss Random forest Reference points(RPs)

SNR and RSSI Based an Optimized Machine Learning Based indoor localization Approach:Multistory Round Building Scenario over LoRa Network

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Computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： indoor localization MKNN LoRa machine learning classification RSSI SNR localization

From Coarse to Fine:Two-Stage indoor localization with Multisensor Fusion

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Tsinghua Science and Technology 2023年第3期28卷 552-565页

作者： Li Zhang Jinhui Bao Yi Xu Qiuyu Wang Jingao Xu Danyang Li School of Mathematics Hefei University of TechnologyHefei 230009China School of Software and BNRist Tsinghua UniversityBeijing 100084China

Increasing attention has been paid to high-precision indoor localization in dense urban and indoor *** studies have shown single indoor localization methods based on WiFi fingerprints,surveillance cameras or Pedestrian Dead Reckoning(PDR)are restricted by low accuracy,limited tracking region,and accumulative error,etc.,and some defects can be resolved with more labor costs or special ***,requesting more additional information and extra user constraints is costly and rarely *** this paper,a two-stage indoor localization system is presented,integrating WiFi fingerprints,the vision of surveillance cameras,and PDR(the system abbreviated as iWVP).A coarse location using WiFi fingerprints is done advanced,and then an accurate location by fusing data from surveillance cameras and the IMU sensors is *** uses a matching algorithm based on motion sequences to confirm the identity of pedestrians,enhancing output accuracy and avoiding corresponding drawbacks of each *** experimental results show that the iWVP achieves high accuracy with an average position error of 4.61 cm,which can effectively track pedestrians in multiple regions in complex and dynamic indoor environments.

关键词： indoor localization WiFi fingerprints computer vision Pedestrian Dead Reckoning(PDR)

A Robust indoor localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

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China Communications 2023年第2期20卷 179-197页

作者： Long Cheng Peng Zhao Dacheng Wei Yan Wang Department of Computer and Communication Engineering Northeastern UniversityQinhuangdao 066004China

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of ***-line-of sight(NLOS)is a primary challenge in indoor complex *** this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS ***,fitting polynomials are used to predict the measured *** residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering *** measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in *** distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to ***,the maximum likelihood method is used to calculate the position coordinate *** simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this *** it shows strong robustness in strong NLOS environment.

关键词： wireless sensor network indoor localization NLOS environment gaussian mixture model(GMM) fitting polynomial

Supplementary open dataset for WiFi indoor localization based on received signal strength

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Satellite Navigation 2022年第3期3卷 178-192,I0005页

作者： Jingxue Bi Yunjia Wang Baoguo Yu Hongji Cao Tongguang Shi Lu Huang School of Surveying and Geo-Informatics Shandong Jianzhu UniversityJinan250101China School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhou221116China Key Laboratory of Satellite Navigation System and Equipment Technology The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050081China

Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor ***,they can’t meet all the demands of WiFi RSS-based localization.A supplementary open dataset for WiFi indoor localization based on RSS,called as SODindoorLoc,covering three buildings with multiple floors,is presented in this *** dataset includes dense and uniformly distributed Reference Points(RPs)with the average distance between two adjacent RPs smaller than 1.2 ***,the locations and channel information of pre-installed Access Points(APs)are summarized in the *** addition,computer-aided design drawings of each floor are *** SODindoorLoc supplies nine training and five testing *** standard machine learning algorithms and their variants(eight in total)are explored to evaluate positioning accuracy,and the best average positioning accuracy is about 2.3 ***,the SODindoorLoc can be treated as a supplement to UJIindoorLoc with a consistent *** dataset can be used for clustering,classification,and regression to compare the performance of different indoor positioning applications based on WiFi RSS values,e.g.,high-precision positioning,building,floor recognition,fine-grained scene identification,range model simulation,and rapid dataset construction.

关键词： WiFi indoor localization Open dataset RSS AP Machine learning

An Improved Convolutional Neural Network Based indoor localization by Using Jenks Natural Breaks Algorithm

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China Communications 2022年第4期19卷 291-301页

作者： Chengjie Hou Yaqin Xie Zhizhong Zhang School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqingChina School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjingChina

With the rapid growth of the demand for indoor location-based services(LBS), Wi-Fi received signal strength(RSS) fingerprints database has attracted significant attention because it is easy to obtain. The fingerprints algorithm based on convolution neural network(CNN) is often used to improve indoor localization accuracy. However, the number of reference points used for position estimation has significant effects on the positioning accuracy. Meanwhile, it is always selected arbitraily without any guiding standards. As a result, a novel location estimation method based on Jenks natural breaks algorithm(JNBA), which can adaptively choose more reasonable reference points, is proposed in this paper. The output of CNN is processed by JNBA, which can select the number of reference points according to different environments. Then, the location is estimated by weighted K-nearest neighbors(WKNN). Experimental results show that the proposed method has higher positioning accuracy without sacrificing more time cost than the existing indoor localization methods based on CNN.

关键词： indoor localization convolution neural network(CNN) Wi-Fi fingerprints Jenks natural breaks

RSS-Based indoor localization System with Single Base Station

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Computers, Materials & Continua 2022年第3期70卷 5437-5452页

作者： Samir Salem Al-Bawri Mohammad Tariqul Islam Mandeep Jit Singh Mohd Faizal Jamlos Adam Narbudowicz Max J.Ammann Dominique M.M.P.Schreurs Space Science Centre Climate Change InstituteUniversiti Kebangsaan MalaysiaBangi43600Malaysia Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaUKMBangi43600SelangorMalaysia Faculty of Mechanical Engineering Universiti Malaysia PahangPekan26600PahangMalaysia Antenna and High Frequency Research Centre School of Electrical&Electronic EngineeringDublin Institute of TechnologyDublin 8Ireland Department of Electrical Engineering Division ESAT-TELEMICKU LeuvenBelgium

The paper proposes an indoor localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable *** proposed algorithm measures Received Signal Strength(RSS)and maps it to the location in the room by estimating signal strength of a direct line of sight(LOS)signal and signal of the first order reflection from the *** algorithm is evaluated through both simulations and empirical measurements in a furnished open space office,sampling 21 different locations in the *** is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80%confidence Cumulative Distribution Function(CDF)user level,demonstrating the ability to accurately estimate the receiver’s location within the *** system is intended as a cost-efficient indoor localization technique,offering simplicity and easy integration with existing wireless communication *** comparable single base station localization techniques,the proposed system does not require beam scanning,offering stable communication capacity while performing the localization process.

关键词： indoor localization localization techniques received signal strength