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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network
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作者 Muhammad Ayoub Kamal Muhammad Mansoor Alam +1 位作者 Aznida Abu Bakar Sajak Mazliham Mohd Su’ud 《Computers, Materials & Continua》 SCIE EI 2024年第8期1927-1945,共19页
In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine ... In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In 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 algorithm.Received 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 building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This 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 point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity. 展开更多
关键词 indoor localization MKNN LoRa machine learning classification RSSI SNR localization
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Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization
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作者 Qiu Wanqing Zhang Qingmiao +1 位作者 Zhao Junhui Yang Lihua 《China Communications》 SCIE CSCD 2024年第5期113-122,共10页
Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the rece... Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,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 receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It 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. 展开更多
关键词 extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization
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Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access
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作者 Pan Hao Chen Yu +1 位作者 Qi Xiaogang Liu Meili 《China Communications》 SCIE CSCD 2024年第3期37-50,共14页
Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)sig... Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method. 展开更多
关键词 indoor localization MOPSO network access RSS prediction
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Fine-grained grid computing model for Wi-Fi indoor localization in complex environments
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作者 Yan Liang Song Chen +1 位作者 Xin Dong Tu Liu 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第1期42-52,共11页
The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the posi... The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An 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)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,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 environments.As 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 methods.The 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 simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue. 展开更多
关键词 Fine-grained grid computing (FGGC) indoor localization Path loss Random forest Reference points(RPs)
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Survey of Indoor Localization Based on Deep Learning
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作者 Khaldon Azzam Kordi Mardeni Roslee +3 位作者 Mohamad Yusoff Alias Abdulraqeb Alhammadi Athar Waseem Anwar Faizd Osman 《Computers, Materials & Continua》 SCIE EI 2024年第5期3261-3298,共38页
This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetwork... This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It 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 locationcentricservices.While 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. 展开更多
关键词 Deep learning indoor localization wireless-based localization
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A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model 被引量:2
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作者 Long Cheng Peng Zhao +1 位作者 Dacheng Wei Yan Wang 《China Communications》 SCIE CSCD 2023年第2期179-197,共19页
Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex enviro... Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex environment.In this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error.Firstly,fitting polynomials are used to predict the measured values.The 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 centers.The measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in turn.The distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to probability.Finally,the maximum likelihood method is used to calculate the position coordinate estimation.Through simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper.And it shows strong robustness in strong NLOS environment. 展开更多
关键词 wireless sensor network indoor localization NLOS environment gaussian mixture model(GMM) fitting polynomial
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TOA-Based NLOS Error Mitigation Algorithm for 3D Indoor Localization 被引量:14
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作者 Weigang Wang Yunwei Zhang Longbin Tian 《China Communications》 SCIE CSCD 2020年第1期63-72,共10页
In the process of indoor localization,the existence of the non-line of sight(NLOS)error will greatly reduce the localization accuracy.To reduce the impact of this error,a 3 dimensional(3D)indoor localization algorithm... In the process of indoor localization,the existence of the non-line of sight(NLOS)error will greatly reduce the localization accuracy.To reduce the impact of this error,a 3 dimensional(3D)indoor localization algorithm named LMR(LLS-Minimum-Residual)is proposed in this paper.We first estimate the NLOS error and use it to correct the measurement distances,and then calculate the target location with linear least squares(LLS)solution.The final nodes location can be obtained accurately by NLOS error mitigation.Our algorithm can work efficiently in both indoor 2D and 3D environments.The simulation results show that the proposed algorithm has better performance than traditional algorithms and it can significantly improve the localization accuracy. 展开更多
关键词 indoor localization NLOS LLS 2D 3D
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Variance-based fingerprint distance adjustment algorithm for indoor localization 被引量:7
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作者 Xiaolong Xu Yu Tang +1 位作者 Xinheng Wang Yun Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第6期1191-1201,共11页
The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication (RSSI) and the variance of R... The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication (RSSI) and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm (VFDA). Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs. 展开更多
关键词 indoor localization fingerprint localization receivedsignal strength indication (RSSI) variance fingerprint distance.
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An Efficient Machine Learning Approach for Indoor Localization 被引量:5
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作者 Lingwen Zhang Yishun Li +1 位作者 Yajun Gu Wenkao Yang 《China Communications》 SCIE CSCD 2017年第11期141-150,共10页
Indoor localization has gained much attention over several decades due to enormous applications. However, the accuracy of indoor localization is hard to improve because the signal propagation has small scale effects w... Indoor localization has gained much attention over several decades due to enormous applications. However, the accuracy of indoor localization is hard to improve because the signal propagation has small scale effects which leads to inaccurate measurements. In this paper, we propose an efficient learning approach that combines grid search based kernel support vector machine and principle component analysis. The proposed approach applies principle component analysis to reduce high dimensional measurements. Then we design a grid search algorithm to optimize the parameters of kernel support vector machine in order to improve the localization accuracy. Experimental results indicate that the proposed approach reduces the localization error and improves the computational efficiency comparing with K-nearest neighbor, Back Propagation Neural Network and Support Vector Machine based methods. 展开更多
关键词 indoor localization machine learning SVM PCA
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Hybrid ToA and IMU indoor localization system by various algorithms 被引量:4
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作者 CHEN Xue-chen CHU Sheng +1 位作者 LI Fan CHU Guang 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第8期2281-2294,共14页
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accele... In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line. 展开更多
关键词 indoor localization time of arrival (ToA) inertial measurement unit (IMU) Bayesian filter extended Kalman filter MAP algorithm
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Improved GNSS Cooperation Positioning Algorithm for Indoor Localization 被引量:4
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作者 Taoyun Zhou Baowang Lian +2 位作者 Siqing Yang Yi Zhang Yangyang Liu 《Computers, Materials & Continua》 SCIE EI 2018年第8期225-245,共21页
For situations such as indoor and underground parking lots in which satellite signals are obstructed,GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nod... For situations such as indoor and underground parking lots in which satellite signals are obstructed,GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nodes.Here we study the cooperative positioning of two static nodes,node 1 is placed on the roof of the building and the satellite observation is ideal,node 2 is placed on the indoor windowsill where the occlusion situation is more serious,we mainly study how to locate node 2 with the assistance of node 1.Firstly,the two cooperative nodes are located with pseudo-range single point positioning,and the positioning performance of cooperative node is analyzed,therefore the information of pseudo-range and position of node 1 is obtained.Secondly,the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase.Finally,the cooperative location algorithms are studied.The Extended Kalman Filtering(EKF),Unscented Kalman Filtering(UKF)and Particle Filtering(PF)are used to fuse the pseudo-range,ranging information and location information respectively.Due to the mutual influences among the cooperative nodes in cooperative positioning,the EKF,UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time.Experimental results show that after being improved,the influence between the cooperative nodes becomes smaller,and the positioning performance of the nodes is better than before. 展开更多
关键词 indoor localization GNSS cooperative positioning extended kalman filtering(EKF) unscented kalman filtering(UKF) particle filtering(PF)
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An Improved Convolutional Neural Network Based Indoor Localization by Using Jenks Natural Breaks Algorithm 被引量:3
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作者 Chengjie Hou Yaqin Xie Zhizhong Zhang 《China Communications》 SCIE CSCD 2022年第4期291-301,共11页
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... 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. 展开更多
关键词 indoor localization convolution neural network(CNN) Wi-Fi fingerprints Jenks natural breaks
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Domain adaptive methods for device diversity in indoor localization 被引量:1
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作者 Liu Jing Liu Nan +1 位作者 Pan Zhiwen You Xiaohu 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期424-430,共7页
To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robu... To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement. 展开更多
关键词 wireless local area networks indoor localization fingerprinting device diversity transfer learning correlation alignment
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Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones 被引量:1
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作者 Imran Ashraf Soojung Hur +1 位作者 Yousaf Bin Zikria Yongwan Park 《Computers, Materials & Continua》 SCIE EI 2021年第8期2597-2620,共24页
Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripp... Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches. 展开更多
关键词 indoor localization magnetic field data long short term memory network data normalization gated recurrent unit network deep learning
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RSS-Based Indoor Localization System with Single Base Station 被引量:1
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作者 Samir Salem Al-Bawri Mohammad Tariqul Islam +4 位作者 Mandeep Jit Singh Mohd Faizal Jamlos Adam Narbudowicz Max J.Ammann Dominique M.M.P.Schreurs 《Computers, Materials & Continua》 SCIE EI 2022年第3期5437-5452,共16页
The paper proposes an Indoor Localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable antennas.The proposed algorithm measures Received Signal Strength(RSS)and maps it to the lo... The paper proposes an Indoor Localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable antennas.The 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 wall.The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office,sampling 21 different locations in the room.It 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 room.The system is intended as a cost-efficient indoor localization technique,offering simplicity and easy integration with existing wireless communication systems.Unlike comparable single base station localization techniques,the proposed system does not require beam scanning,offering stable communication capacity while performing the localization process. 展开更多
关键词 indoor localization localization techniques received signal strength
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An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion
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作者 Haoyu Yang Yuanshuo Wang +1 位作者 Dongchen Li Tiancheng Li 《Journal of Beijing Institute of Technology》 EI CAS 2022年第6期570-583,共14页
In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according t... In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach. 展开更多
关键词 3D indoor localization fingerprint fusion positioning time-difference of arrival pedestrian dead reckoning received signal strength
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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time
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作者 Muhammad S.Alam Farhan B.Mohamed +2 位作者 Ali Selamat Faruk Ahmed AKM B.Hossain 《Intelligent Automation & Soft Computing》 2024年第3期417-436,共20页
Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance o... Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance. 展开更多
关键词 Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)
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From Coarse to Fine:Two-Stage Indoor Localization with Multisensor Fusion
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作者 Li Zhang Jinhui Bao +3 位作者 Yi Xu Qiuyu Wang Jingao Xu Danyang Li 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2023年第3期552-565,共14页
Increasing attention has been paid to high-precision indoor localization in dense urban and indoor environments.Previous studies have shown single indoor localization methods based on WiFi fingerprints,surveillance ca... Increasing attention has been paid to high-precision indoor localization in dense urban and indoor environments.Previous 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 scenes.However,requesting more additional information and extra user constraints is costly and rarely applicable.In 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 obtained.iWVP uses a matching algorithm based on motion sequences to confirm the identity of pedestrians,enhancing output accuracy and avoiding corresponding drawbacks of each subsystem.The 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. 展开更多
关键词 indoor localization WiFi fingerprints computer vision Pedestrian Dead Reckoning(PDR)
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D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization
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作者 Wei Liu Zhiqiang Dun 《Journal of Information and Intelligence》 2023年第2期104-114,共11页
Deep learning based channel state information(CSI)fingerprint indoor localization schemes need to collect massive labeled data samples for training,and the parameters of the deep neural network are used as the fingerp... Deep learning based channel state information(CSI)fingerprint indoor localization schemes need to collect massive labeled data samples for training,and the parameters of the deep neural network are used as the fingerprints.However,the indoor environment may change,and the previously constructed fingerprint may not be valid for the changed environment.In order to adapt to the changed environment,it requires to recollect massive amount of labeled data samples and perform the training again,which is labor-intensive and time-consuming.In order to overcome this drawback,in this paper,we propose one novel domain adversarial neural network(DANN)based CSI Fingerprint Indoor Localization(D-Fi)scheme,which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment.Specifically,the previous environment and changed environment are treated as the source domain and the target domain,respectively.The DANN consists of the classification path and the domain-adversarial path,which share the same feature extractor.In the offline phase,the labeled CSI samples are collected as source domain samples to train the neural network of the classification path,while in the online phase,for the changed environment,only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domainadversarial path to update parameters of the feature extractor.In this case,the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment.Experiment results show that for the changed localization environment,the proposed D-Fi scheme significantly outperforms the existing convolutional neural network(CNN)based scheme. 展开更多
关键词 indoor localization Domain adversarial neural network CSI FINGERPRINT Deep learning
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A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts
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作者 Pan Hao Chen Yu +1 位作者 Qi Xiaogang Liu Meili 《China Communications》 SCIE CSCD 2024年第6期40-52,共13页
With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication... With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach. 展开更多
关键词 indoor localization machine learning ultra wideband WiFi fingerprint
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