A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

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.

A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.

A Low-Cost Outdoor Fingerprinting Localization Scheme For Wireless Cellular Networks

This paper considers outdoor fingerprinting localization in LTE cellular Networks,which can localize non-cooperative user equipment(UE)that is unwilling to provide Global Positioning System(GPS)information.We propose a low-cost fingerprinting localization scheme that can improve the localization accuracy while reducing the computational complexity.Firstly,a data filtering strategy is employed to filter the fingerprints which are far from the target UE by using the Cell-ID,Timing Advance(TA)and eNodeB environment information,and the distribution of TA difference is analyzed to guide how to use TA rationally in the filtering strategy.Then,improved Weighted K Nearest Neighbors(WKNN)are implemented on the filtered fingerprints to give the final location prediction,and the WKNN is improved by removing the fingerprints that are still far away from the most of the K neighbors.Experiment results show that the performance is improved by the proposed localization scheme,and positioning errors corresponding to Cumulative Distribution Function(CDF)equaling to 67% and 95% are declined to 50 m and 150 m.

Variance-based fingerprint distance adjustment algorithm for indoor localization

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.

Convolutional Neural Networks Based Indoor Wi-Fi Localization with a Novel Kind of CSI Images

Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel state information(CSI)image is proposed to improve the localization accuracy.Compared with previous methods of constructing the CSI image,the new kind of CSI image proposed is able to contain more channel information such as the angle of arrival(AoA),the time of arrival(TOA)and the amplitude.We construct three gray images by using phase differences of different antennas and amplitudes of different subcarriers of one antenna,and then merge them to form one RGB image.The localization method has off-line stage and on-line stage.In the off-line stage,the composed three-channel RGB images at training locations are used to train a convolutional neural network(CNN)which has been proved to be efficient in image recognition.In the on-line stage,images at test locations are fed to the well-trained CNN model and the localization result is the weighted mean value with highest output values.The performance of the proposed method is verified with extensive experiments in the representative indoor environment.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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.

Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation issues.As complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational efficiency.Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online phase.Bayesian networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user locations.These networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to navigate.This article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user location.The proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s location.It operates in both online and offline phases.In the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s location.Experimental results unequivocally showcase the superior performance of the proposed model when compared to other existing models and methods.The proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing approaches.Notably,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security inspection.However,due to the interference of environment factors such as multipath propagation and NLOS(Non-Line-of-Sight),the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS(Received Signal Strength)is difficult to accurately measure.Therefore,the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper.The algorithm hierarchically divide the room where the target Wi-Fi AP is located,on the region division line,a modified signal collection device is used to measure RSS in two directions of each reference point.All RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate region.The location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy threshold.There are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable APs.The experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters,and the minimum localization error is 0.16 meters,which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.

An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion

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.

An Improved Convolutional Neural Network Based Indoor Localization by Using Jenks Natural Breaks Algorithm

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.

Compressive Sensing Based Wireless Localization in Indoor Scenarios

The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.

Fusing Fixed and Hint Landmarks on Crowd Paths for Automatically Constructing Wi-Fi Fingerprint Database

In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.

Fingerprint Matching Based on Local Relative Orientation Field

A fingerprint matching method based on local relative orientation field is proposed. It extracts local relative orientation field around each minutia for minutiae matching. Local orientation features are also used to sorting minutiae in order to speed up searching a minutia when pairing minutiae. The experimental result reveals that this method achieves improved recognition accuracy. Key words fingerprint matching - ridge-based minutiae matching - local relative orientation field - sorting minutiae CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: ZHU En (1976-), male, Ph. D candidate, research direction: pattern recognition, image processing and information security.

Domain adaptive methods for device diversity in indoor localization

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.

Efficient Techniques and Algorithms for Improving Indoor Localization Precision on WLAN Networks Applications

This paper proposes efficient techniques that allow the deploying of high precision location applications for indoor scenarios over Wireless Local Area Networks (WLAN). Firstly, we compare the use of radio frequency (RF) power levels and relative time delays based on ray-tracing as detection methods to estimate the localization of a set of mobile station using the fingerprint technique. Detection method play an important role in applications of high frequencies techniques for locations systems based on current and emerging standards such as Wi-Fi (802.11x) and Wi-Max (802.16x). The localization algorithm computes the Eucli- dean distance between the samples of signals received from each unknown position and each fingerprint stored in the database or radio-map obtained using the FASPRI simulation tool. Experimental results show that more precision can be obtained in the localization process by means of relative delay instead of RF power detection method. Secondly, the Euclidean distance has been compared with others similarity distance measures. Finally, an interpolation algorithm between the fingerprinting weighing based on the distances has been implemented in order to eliminate those fingerprints that do not contribute to the improvement in the accuracy. These techniques allow obtaining more precision in the localization of indoor mobile devices over WLAN networks.

A WKNN-based approach for NB-IoT sensors localization

With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.

Leveraging Logical Anchor into Topology Optimization forIndoor Wireless Fingerprinting

The indoor subarea localization has wide application space in dynamic hotzone identification, indoor layout optimization, store dynamic pricing and crowd flowtrend prediction. The ubiquitous mobile devices provide the opportunity for wirelessfingerprinting-based indoor localization services. However, there are two short boardwhere the existing methods have been criticized. One is that a tagging approach requiresa large number of professional surveys for wireless fingerprint construction, whichweakens the scalability of the methods. The other is that the crowdsourcing-basedmethods encounter the cold boot problem in the system initial stage. To address theseissues, the paper proposes a topology optimization approach leveraging the dynamiclogical anchor selection into a subarea localization system. First of all, a newannular-based radio map construction strategy with the feedback selection of logic anchoris designed to release the pressure of site survey. The implementation of this strategyharnesses the characteristics of the indoor building structure and inter subareaoverlapping recognition, without the topology and distribution of physical anchor (e.g.,access points or POIs). Secondly, exploiting the probabilistic support vector machinealgorithm, the target is localized in the corresponding subarea in a real-time pattern.Furthermore, the localization error is calibrated with an error recognition algorithm.Finally, massive experiments are implemented on a prototype system. The results showthat the proposed method can decrease the overhead of the system initialization andachieve higher localization accuracy compared with the existing approaches.

Robust Techniques for Accurate Indoor Localization in Hazardous Environments

The challenging conditions prevalent in indoor environments have rendered many traditional positioning methods inept to yield satisfactory results. Our work tackles the challenging problem of accurate indoor positioning in hazardous multipath environments through three versatile super resolution techniques: time domain Multiple Signal Classification (TD-MUSIC), frequency domain MUSIC (FD-MUSIC) algorithms, and frequency domain Eigen value (FD-EV) method. The advantage of using these super resolution techniques is twofold. First for Line-of-Sight (LoS) conditions this provides the most accurate means of determining the time delay estimate from transmitter to receiver for any wireless sensor network. The high noise immunity and resolvability of these methods makes them ideal for cost-effective wireless sensor networks operating in indoor channels. Second for non-LoS conditions the resultant pseudo-spectrum generated by these methods provides the means to construct the ideal location based fingerprint. We provide an in depth analysis of limitation as well as advantages inherent in all of these methods through a detailed behavioral analysis under constrained environments. Hence, the bandwidth versatility, higher resolution capability and higher noise immunity of the TD-MUSIC algorithm and the FD-EV method’s ability to resurface submerged signal peaks when the signal subspace dimensions are underestimated are all presented in detail.

基于WI-FI的井下定位算法研究

基于射频指纹的室内定位算法有很多,现提出基于射频指纹的应用在煤矿巷道这种特有的环境特征的一种新的定位算法。根据无线信号的衰减特性,将距离接入点3m处的接收信号强度值设为阈值,若设备接收的信号强度值大于阈值则直接定位到此接入点处。若信号强度低于阈值,则根据射频指纹数据库数据得到估计位置。在煤矿中移动目标的速度一般小于10m/s,根据这一特征,算法中设定前后两次的定位位置不能超过10 m;若超过10 m,则取前一秒的定位位置与该秒采用数据库匹配得出的位置的中点。将这些特征应用到射频指纹定位算法中可以非常有效的减少环境对信号强度的影响。通过实验表明,该定位算法具有较高的定位精度。