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 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.

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.

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.

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.

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.

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.

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units,in large indoor spaces demands a precise knowledge of their positions.Technologies like WiFi and Bluetooth,despite their low-cost and availability,are sensitive to signal noise and fading effects.For these reasons,a hybrid approach,which uses two different signal sources,has proven to be more resilient and accurate for the positioning determination in indoor environments.Hence,this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning,using Received Signal Strength information from available Wireless Local Area Network access points,together with the Wireless Sensor Networks technology.Six signals were recorded on a regular grid of anchor points,covering the research space.An optimization was performed by relative signal weighting,to minimize the average positioning error over the research space.The optimization process was conducted using a standard Quantum Particle Swarm Optimization,while the position error estimate for all given sets of weighted signals was performed using aMultilayer Perceptron(MLP)neural network.Compared to our previous research works,the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned.These experimental results led to the 20%reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process.Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Deep learning based channel state information(CSI)fingerprint indoor localization schemes need to collect massive labeled data samples for training,and the parameters of the deep neural network are used as the fingerprints.However,the indoor environment may change,and the previously constructed fingerprint may not be valid for the changed environment.In order to adapt to the changed environment,it requires to recollect massive amount of labeled data samples and perform the training again,which is labor-intensive and time-consuming.In order to overcome this drawback,in this paper,we propose one novel domain adversarial neural network(DANN)based CSI Fingerprint Indoor Localization(D-Fi)scheme,which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment.Specifically,the previous environment and changed environment are treated as the source domain and the target domain,respectively.The DANN consists of the classification path and the domain-adversarial path,which share the same feature extractor.In the offline phase,the labeled CSI samples are collected as source domain samples to train the neural network of the classification path,while in the online phase,for the changed environment,only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domainadversarial path to update parameters of the feature extractor.In this case,the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment.Experiment results show that for the changed localization environment,the proposed D-Fi scheme significantly outperforms the existing convolutional neural network(CNN)based scheme.

利用众包更新Wi-Fi室内定位指纹库的方法研究

Wi-Fi定位是目前较为主流的室内定位方法,而位置指纹库的建立和维护对Wi-Fi定位至关重要。Wi-Fi信号时变性强要求指纹库及时更新。针对由专业人员更新指纹库的人力耗费问题,提出利用众包更新指纹库的方法,允许用户对定位结果进行评价和修正,使得用户在享受定位结果的同时参与到指纹库的维护更新中,特别针对用户的错误修正提出了基于聚类的错误检测方法,能有效避免指纹库被错误指纹污染。开发了室内定位系统,通过在真实室内环境的实验验证了本文提出的方法可以长时间保持较高的定位性能。

5G信道状态信息信号质量及指纹定位性能分析

5G信道状态信息(channel state information,CSI)具有丰富的特征信息,是一种理想的指纹定位信号,但信号质量易受环境干扰,对定位性能影响较大.为了分析不同因素对5G信号质量和定位性能的影响程度,本文首先阐述了5G信号特征和基于支持向量回归(support vector regression,SVR)的定位算法,分析了数据采集时终端的高度、方向、人体遮挡等因素对信号质量的影响,测试了廊厅、小办公室和中型会议室三种场景下的定位性能.结果表明:5G信号质量受周围环境影响较大,在干扰较小的情况下,基于5G CSI的位置指纹定位算法在三种场景下的定位精度分别为0.93 m、1.46 m和1.94 m,能够满足大多数室内定位应用需求.

基于域自适应的Wi-Fi指纹设备无关室内定位模型

基于Wi-Fi指纹定位方法在大规模实际应用中存在设备多样性问题,定位精度受到极大影响。提出了一种设备无关的Wi-Fi指纹室内定位模型DeviceTransfer。该模型基于深度学习的域自适应理论,把智能手机的设备类型作为域,通过对抗训练来提取任务相关而设备无关的Wi-Fi数据特征,并把学习到的源域位置信息迁移到目标域上。采用预训练和联合训练来提高模型训练的稳定性并加快收敛。在教学楼和商场2个真实场景中,使用4台不同型号的智能手机验证模型的性能。实验结果表明,DeviceTransfer能够有效提取设备无关的Wi-Fi数据特征。只使用一台手机在参考点采集Wi-Fi指纹,使用其他型号手机在线定位也能获得较高的定位精度,降低了定位成本。

基于群组的WI-FI指纹室内定位研究

WI-FI指纹定位是最常用的室内定位算法之一,但是WI-FI指纹定位存在较大的定位误差。为了提高定位精度,提出了基于群组的定位算法。首先在WI-FI指纹定位的基础上,提出了群组的概念,然后阐述了基于最小编辑距离的群组识别算法,最后利用群组中人与人之间的物理距离,设计了一种基于群组的定位算法。实验表明,基于群组的WI-FI指纹定位算法能明显减小WI-FI指纹定位的误差。

信号指纹测量下双度量协同的室内定位方法

针对室内WiFi定位中指纹信息冗余、空间边界划分困难和RP集获取精准度缺失的问题,提出一种信号指纹测量下双度量协同的室内定位方法。通过S度量和欧氏度量下指纹矩阵融合,精简形成低维指纹信息,考量指纹间“点-类”关联度和“类-类”相似度,兼顾子区域边界新增指纹数目的可控性,确立子区域边界模糊深度调整机制,形成边界模糊泛化能力,以区域稀疏度判定插值方法完成指纹库扩充,以构建高密度离线指纹库。在优选子区域中,结合信号空间和位置空间,对比两类度量的差异度,实现对高价值指纹点的定向筛选,削弱在线指纹匹配集合的误差影响。在全局实验场景中,分区结果规整有序,较为符合实际空间构造。指纹库构建效果较其他方案至少提升11%,定位精度相对同类型算法提升了12%以上,论文所提方案定位精度优势显著,在具备高扰动特点下的复杂室内环境中,具有较好的场景适应性。

基于ECA-CNN的混合指纹室内定位方法

在指纹法的定位中,指纹特征和定位模型是影响定位精度的2个关键因素.在指纹特征的选取方面,可见光强度稳定性较高,但位置特征区分度低;无线信号强度区分度较高,但波动性较强.在定位模型的构建方面,基于卷积神经网络(convolutional neural network, CNN)的定位模型在特征提取时不能有效地突出重要特征.针对上述问题,提出一种基于ECA-CNN的混合指纹室内定位方法(hybrid fingerprint indoor localization method based on ECA-CNN, ECACon-HF).首先,利用可见光强度和低功耗蓝牙(Bluetooth low energy, BLE)接收信号强度指示(received signal strength indication, RSSI)来构建混合指纹,降低BLE指纹不稳定的影响,并增强不同位置之间的区分度.同时,基于高效通道注意力(efficient channel attention, ECA)模块改进CNN定位模型,ECA能够通过跨通道交互策略自适应地提取指纹中的重要信息,抑制指纹中的环境干扰,增强混合指纹特征表达能力,更有效地利用混合指纹优势.实验结果显示,ECAConHF在构建的混合指纹库上可以达到0.316 m的定位精度,高于在单一指纹上的精度;并且基于同一指纹库,ECACon-HF相比其他室内定位方法,定位精度具有明显优势.

双源信号下多元尺度融合室内位置测算方法

针对大型多接入点场景的指纹定位中存在的定位点区域归属误判、离群点干扰的问题,提出一种双源信号下多元尺度融合室内位置测算方法。指纹在线定位阶段,利用PDR信号的时空信息,将定位点归属分区内的参考点数量进行扩展,缓解邻界区域误判带来的负效益;此外,利用多元距离与卡方距离代替传统欧氏距离,结合空间域物理距离尺度,实现多元尺度下的近邻筛选,有效克服离群点干扰;引入K值动态适配,并基于此进行Wi-Fi与PDR预定位的动态链接式融合,进一步提高定位算法的准确性。实验结果表明,在引入双源信号的相同条件下,相比其他多元尺度与动态K值算法,所提方案综合性能较优,平均定位精度优于其他算法6.6%~23.1%。

From Coarse to Fine:Two-Stage Indoor Localization with Multisensor Fusion

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