Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural net...With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
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
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
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
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
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 al...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
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
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
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
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
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
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
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
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
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
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
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
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
基金supported by Natural Science Foundation of Hunan Province under Grant(NO:2021JJ31142).
文摘With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
基金supported in part by the foundation of Nanjing University of Posts and Telecommunications (No. NY215164)by the National Experimental Teaching Demonstration Centre Reform Project: Virtual 201106+2 种基金supported by the Key University Science Research Project of Jiangsu Province under Grant (No. 14KJA510003)supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant No. SJCX19_0275supported by the National Natural Science Foundation under grant No. 61771257, No. 61605085 and No.61571233, No.61871232
文摘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.
基金supported by the National Natural Science Foundation of China(6120200461472192)+1 种基金the Special Fund for Fast Sharing of Science Paper in Net Era by CSTD(2013116)the Natural Science Fund of Higher Education of Jiangsu Province(14KJB520014)
文摘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.
基金supported by“the Fundamental Research Funds for the Central Universities No. 2017JBM016”
文摘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.
基金Project(61301181) supported by the National Natural Science Foundation of China
文摘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.
基金supported by the National Natural Science Foundation of China(NSFC)under Grants 62001238 and 61901075。
文摘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.
基金This work was financially supported by National Major SpecialScience and Technology (No. GFZX0301040115)the National Natural Science Foundationof China (No. 61301094, No. 61571188)the Construct Program of the Key Discipline inHunan Province, China, the Aid program for Science and Technology Innovative ResearchTeam in Higher Educational Institute of Hunan Province, and the Planned Science andTechnology Project of Loudi City, Hunan Province, China.
文摘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.
基金supported in part by the National Natural Science Foundation of China(U2001213 and 61971191)in part by the Beijing Natural Science Foundation under Grant L182018 and L201011+2 种基金in part by National Key Research and Development Project(2020YFB1807204)in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006)in part by the Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2020-S321)。
文摘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.
基金The research will be funded by the Multimedia University,Department of Information Technology,Persiaran Multimedia,63100,Cyberjaya,Selangor,Malaysia.
文摘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.
基金supported by the National Natural Science Foundation of China under Grant No.62273083 and No.61973069Natural Science Foundation of Hebei Province under Grant No.F2020501012。
文摘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.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2016-0-00313)supervised by the IITP(Institute for Information&communication Technology Promotion)+1 种基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(2017R1E1A1A01074345).
文摘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.
基金This work is supported by Climate Change Institute,Universiti Kebangsaan Malaysia.
文摘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.
基金The National Key Research and Development Program of China(No.2018YFB1802400)the National Natural Science Foundation of China(No.61571123)the Research Fund of National M obile Communications Research Laboratory,Southeast University(No.2020A03)
文摘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.
基金the Fundamental Research Grant Scheme-FRGS/1/2021/ICT09/MMU/02/1,Ministry of Higher Education,Malaysia.
文摘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.
文摘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.
基金the Open Project of Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education under Grant No.YYZN-2023-4the Ph.D.Fund of Chengdu Technological University under Grant No.2020RC002.
文摘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.
基金partially supported by the National Natural Science Foun-dation of China(No.62071389).
文摘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.
