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.

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.

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.

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.

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.

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.

基于地理不可区分性且WiFi可灵活部署的室内定位安全方案

在室内定位服务中,WiFi指纹技术因其覆盖面积广、定位精度高而受到人们的广泛关注.然而,对于在线阶段的位置查询,用户的个人敏感信息容易受到恶意攻击而造成位置隐私泄露.现有基于WiFi指纹的室内定位技术仅考虑室内单一空旷平面,这使得WiFi部署的灵活性受到限制.而当WiFi部署在多维场景时,空间位置隐私问题亟待解决.提出了一种基于地理不可区分性的WiFi指纹室内定位隐私保护方案,用户利用自身接收信号强度生成一个新的接收信号强度向量,并通过加噪混淆将得到的数据发送给位置服务提供商,同时引入数字签名技术,在混淆位置被发送给位置服务提供商实现定位之前确保客户端身份不被伪造.基于模拟实验平台的实验结果表明,该方案支持WiFi的灵活部署,能够在保护位置隐私的同时,首次实现12个WiFi接入点灵活部署情况下的高精度定位,保证定位误差小于1 m.

基于LightGBM和LSTM指纹定位算法的研究

为进一步提高用户室外指纹定位的预测精度,提出了一种基于LightGBM(轻度提升机)算法和LSTM(长短期记忆神经网络)相结合的模型,通过分析比较基站收集到的用户信息,分别利用LightGBM与LSTM算法进行用户位置的预测,对两种方法预测的结果用误差倒数法减小误差同时对权重进行更新,得到结果作为新的预测值,在用户真实数据集上进行测试。测试结果表明,在对基站收集的用户信息进行指纹定位时,与其它模型相比,文章提出的模型在运行时间和预测精度方面均表现出良好的性能。

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.

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.

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.

Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

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.

基于Fingerprint定位算法的设计与实现

首先将RSSI、TOA与TDOA、超声波与无线结合定位算法进行对比,指出了各种算法的优缺点。提出了一种基于RSSI Fingerprint定位算法,给出了该方法的思想,指出了其适用范围,最后通过软件测试了该算法的可行性。

基于LightGBM的LoRa室外指纹定位算法

随着智慧城市、智能工厂、绿色建筑的发展,低功耗物联网作为这类应用场景下最为重要的组网技术之一,对定位技术的需求也愈发迫切。一般的定位方式精度高但同时能耗大不适用于低功耗物联网。针对这个问题设计一种定位算法,以LoRa为基础,利用基于LightGBM(Light Gradient Boosting Machine)的室外指纹定位算法进行室外定位。通过实验结果验证,其平均定位精度达到了90 m左右,相比传统KNN和BP神经网络而言,定位精度提高了35%左右。同时定位的时间相比传统机器学习方法较短,具有较好的实用性。