Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information (CSI) based indoor localization scheme, C²IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points (APs). C²IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength (RSS) fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C²IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.     

IWFUCIA: An integrated model for Wi-Fi fingerprints radio map updating and localization on changed impact analysis

In Wi-Fi fingerprinting indoor localization, automating radio map database maintenance is one of the crucial issues, as it is a labour-intensive and long-term task for collecting and filtering samples to keep an up-to-date and accurate database. In particular, those access points (APs) newly installed in the environment should update radio maps and be included in the database to improve localization performance. This study presents an IWFUCIA system that automates indoor radio map database maintenance (RMapDM) using crowdsourced samples without accurate location annotation. The IWFUCIA incorporates the newly installed APs detection and identification, the significant APs feature selection, fingerprint integration updating, and online localization algorithms. After collecting new crowdsourced samples, we apply Willmott’s index of agreement (WIA) based on the Supported Vector Machine (SVM) regression to detect and identify a newly installed AP and the original existing ones. After getting the new APs, we propose a correlated coefficient and t-test score algorithm to select only those significant AP-based feature samples. We also proposed a fingerprint integration model to fuse original existing and new APs to update the database. Extensive experiments have been conducted in our teaching building to validate and evaluate the effectiveness of IWFUCIA. The results show that our IWFUCIA is robust for long-term maintenance and updating the outdated radio map database server. The average localization accuracy achieves 0.466 m, which significantly outperforms the localization positioning approaches with the original radio map by 84.96%, outdated radio maps by the changed APs powers removed, increased and decreased by 26.32%, 55.36%, and 73.14%, respectively.     

移动情景感知环境下的WI-FI 室内定位技术及其实现

针对现有室内基于Wi-Fi 的指纹定位技术中指纹样本库会过时的问题,本文提出了一种基于众包的指纹更新 技术,该指纹更新技术能够结合实际的移动情境而不断更新样本库中的指纹,保持样本库中指纹的准确性,从而改进了已有的 基于Wi-Fi 的室内定位技术的精度。     

基于智能手机无线信号和图像距离感知融合的室内定位算法

针对于目前面向个人使用的手机室内定位精度低、效果差,且成本较高难以拓展的问题,提出了一种利用普通智能手机作为硬件设备,融合Wi-Fi无线信号和图像数据,通过双层过滤的方式对用户进行高精度室内定位的算法。算法分为线下阶段和线上阶段。在线下阶段,对目标场地建立坐标系,在坐标系多个目标位置进行Wi-Fi采样并建立指纹库,同时对环境进行拍照取样并抽取图像特征。在线上阶段,通过实时获取的Wi-Fi信息进行第一层过滤,以确定当前用户可能的位置区间;然后,结合提出的一种距离补偿算法对用户手机当前捕获的图像进行特征提取,在第一层过滤的基础上,确定用户的精准位置。在实际场地进行的实验表明,相比传统Wi-Fi及二维图像定位方法,该算法能够在探测接入点（AP）数量较少及室内场景相似的情况下提高室内定位精度,可以应用于一般室内定位应用或结合基于位置的服务（LBS）应用。     

Indoor corner recognition from crowdsourced trajectories using smartphone sensors

Recently, fingerprint crowdsourcing from pedestrian movement trajectories has been promoted to alleviate the site survey burden for radio map construction in fingerprinting-based indoor localization. Indoor corners, as one of the most common indoor landmarks, play an important role in movement trajectory analysis. This paper studies the problem of indoor corner recognition in crowdsourced movement trajectories. In a movement trajectory, smartphone internal sensor measurements experience some signal changes when passing by a corner. However, the state-of-the-art solutions based on signal change detection cannot well deal with the fake corner problem and pose diversity problem in most practical movement trajectories. In this paper, we study the corner recognition problem from an expert system viewpoint by applying machine learning techniques. In particular, we extract recognition features from both the time and frequency domain and propose a hierarchical corner recognition scheme consisting of three classifiers. The first pose classifier is to classify various poses into only two groups according to whether or not a smartphone is kept in a fixed position relative to a user upper body when collecting sensor measurements. Feature selection is then applied to train two corner classifiers each for one pose group. Field experiments are conducted to compare our proposed scheme with three state-of-the-art algorithms. In all cases, our scheme outperforms the best of these algorithms in terms of much higher F1-measure and precision for corner recognition. The results also provide insights on the potentials of using more advanced techniques from expert systems in indoor localization.     

一种层次Levenshtein距离的无指纹校准的室内定位方法

随着移动计算领域的兴起,基于位置的服务越来越受青睐。目前各种室内定位的方法层出不穷,由于室内广泛部署了无线基础设施,基于WiFi指纹信息的室内定位技术是其主流方法。设备异构和室内环境变化是影响定位精度的主要因素。本文针对以上两个问题,提出一种层次Levenshtein距离（HLD）的WiFi指纹距离计算算法,实现异构设备的指纹无校准比对。将不同移动设备采集的RSSI信息转化为AP序列,根据AP对应的RSSI值的差异性计算其层次能级,结合Levenshtein距离计算WiFi指纹之间的距离。对于需定位的WiFi指纹RSSI信息,利用HLD算法获取K个近邻,采用WKNN算法进行预测定位。实验中,为了验证算法的鲁棒性和有效性,在3种不同类型的室内环境中采用5种不同的移动设备来采集WiFi的RSSI信息,其定位的平均精度达1.5 m。     

基于动态时间规整距离指纹匹配的Wi-Fi网络室内定位算法

Wi-Fi网络中常规的基于指纹匹配室内定位算法面临信号时变现象或人为干扰的影响,导致定位精度不高。为此,提出基于动态时间规整（DTW）距离相似性指纹匹配的Wi-Fi网络室内定位算法。首先,该算法将定位区域的Wi-Fi信号特征按照采样的先后顺序转化为时间序列类型指纹,通过计算Wi-Fi信号指纹动态时间规整距离的大小来获取定位点与样本点的相似性;然后,根据采样区域结构特征,将Wi-Fi信号指纹采集问题划分为三类基本的动态路径采样方式;最后,结合多种动态路径采样方式增加指纹特征信息的准确性和完整性,从而提高指纹匹配的准确性和定位精度。大量实验结果表明,较瞬时指纹匹配定位算法,所提算法误差范围在3m以内定位的累积错误率：路径区域匀速运动提高了10%,变速运动提高了13%;开放区域交叉曲线运动提高了9%,S型曲线运动提高了3%。所提算法在实际室内定位应用中能有效提高指纹匹配的准确性和定位精度。     

基于LoRa指纹的室内定位算法

在基于LoRa的室内定位研究中,提出一种基于LoRa指纹和支持向量回归(SVR)的室内定位算法。针对传统基于无线信号RSSI指纹和SVR室内定位算法定位精度不高问题,从两个方面进行改进:在指纹特征方面,增加LoRa测距指纹,提高指纹稳定性;在指纹数据库建立和在线定位过程中,分别采用高斯滤波和中位数滤波来对指纹进行预处理,消除指纹的粗大误差。实验结果显示:1 m以内的定位误差的累积概率为78.5%,3 m以内的定位误差的累积概率为90%。增加LoRa测距指纹之后定位精度相比之前提高了40%;增加了高斯滤波与中位数滤波预处理后定位精度较传统的支持向量回归算法提高了38%。两个方面改进之后定位精度总体提高63%,证明了该算法的两个改进是有效的。     

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。     

基于RSSI信号特征的室内分区指纹定位算法

为提高室内定位精度和算法效率,提出基于RSSI信号特征的分区指纹定位算法。在离线阶段,区别于传统的使用RSSI信号构建离线指纹库的方法,设计使用RSSI信号衰减率建立离线指纹库;在在线定位阶段,针对使用欧式距离进行相似度计算时,容易出现两个点RSSI信号欧式距离较近而物理距离较远的情况,提出使用RSSI信号衰减率进行子区域划分,引入SSD的思想使用二级指纹进行精确定位。通过实验验证了该算法的适应性与有效性。     

COSSET+: Crowdsourced Missing Value Imputation Optimized by Knowledge Base

Missing value imputation with crowdsourcing is a novel method in data cleaning to capture missing values that could hardly be filled with automatic approaches. However, the time cost and overhead in crowdsourcing are high. Therefore, we have to reduce cost and guarantee the accuracy of crowdsourced imputation. To achieve the optimization goal, we present COSSET+, a crowdsourced framework optimized by knowledge base. We combine the advantages of both knowledge-based filter and crowdsourcing platform to capture missing values. Since the amount of crowd values will affect the cost of COSSET+, we aim to select partial missing values to be crowdsourced. We prove that the crowd value selection problem is an NP-hard problem and develop an approximation algorithm for this problem. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed approaches.     

室内信号强度指纹定位算法改进

由于人们对基于位置服务的需求越来越高,室内定位技术在诸多领域得到了广泛的应用,而定位算法则是室内定位研究的重点。首先介绍了最近邻和KNN两种信号强度指纹定位算法,并说明了KNN信号强度指纹算法的不足。在KNN信号强度指纹定位算法的基础上,提出了改进的基于区域划分的定位算法。在定位阶段,首先对接收信号强度进行补偿和滤波处理,以降低各种外在因素对定位精度的影响;同时对定位区域进行划分,选择主参考节点,并基于加权的最近邻匹配来选择最近的信号强度指纹;最后对定位结果进行计算并验证。仿真实验表明,改进的区域划分算法相对于传统的KNN算法,定位精度提高了22.2%,达到2.1m,证明了改进算法的可行性。     

An algorithm for distorted fingerprint matching based on local triangle feature set

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel method, a fuzzy feature match (FFM) based on a local triangle feature set to match the deformed fingerprints. The fingerprint is represented by the fuzzy feature set: the local triangle feature set. The similarity between the fuzzy feature set is used to characterize the similarity between fingerprints. A fuzzy similarity measure for two triangles is introduced and extended to construct a similarity vector including the triangle-level similarities for all triangles in two fingerprints. Accordingly, a similarity vector pair is defined to illustrate the similarities between two fingerprints. The FFM method maps the similarity vector pair to a normalized value which quantifies the overall image to image similarity. The proposed algorithm has been evaluated with NIST 24 and FVC2004 fingerprint databases. Experimental results confirm that the proposed FFM based on the local triangle feature set is a reliable and effective algorithm for fingerprint matching with nonlinear distortions.     

基于半监督仿射传播聚类和KLDA的室内定位算法*

室内定位中位置指纹库采集的密集程度往往跟定位精度密切相关,针对离线阶段时指纹库稀疏的情况下定位精度低的问题,提出了一种基于半监督仿射传播聚类和KLDA的室内定位算法。该算法结合了在线阶段采集无位置标签的RSSI数据,通过建立局部邻域图将无位置标签的RSSI信息反映到离线指纹数据的结构中,并使用KLDA方法抽取位置指纹库中最大的特征信息,有效利用了无位置标签的RSSI信息从而提高定位精度。实验结果表明,该算法结合在线阶段RSSI数据后定位精度得到了明显的提高。而且在仅保留离线指纹数据库三分之二的情况下,也几乎能够取得与传统KNN算法使用全指纹库时相同的定位精度,相当于减少了离线阶段采集指纹库的工作开销。     

基于dynFWA-SVM的WiFi室内定位算法

针对实际定位应用中室内环境复杂,传统的WiFi室内定位算法精度低、稳定性差、代价较高以及不同移动终端之间采集信号强度存在差异等问题,提出了基于dynFWA-SVM的WiFi室内定位模型.定位过程中,利用高斯滤波对信号进行除奇异值操作,同时采用信号强度差(SSD)位置指纹替代传统的接收信号强度(RSS)位置指纹;采用动态搜索烟花算法(dynFWA)优化支持向量机(SVM)参数,从而建立了dynFWA-SVM室内定位模型.实验结果表明:经高斯滤波处理后的SSD指纹可以有效提高定位的稳定性和可靠性,减小因不同终端采集信号强度存在差异对定位结果造成的影响,相较于粒子群优化(PSO)算法和烟花算法(FWA),dynFWA算法的优化效率更高,提出的dynFWA-SVM定位模型的定位误差更低.     

机器人听觉系统中指纹定位改进方法

针对传统指纹定位方法中高定位精度依赖于高参考点密度、计算复杂度高的问题,提出了一种改进的声音位置指纹定位方法。基于声音位置的聚类算法首先被用来构建位置指纹数据库,从而降低在线搜索数据库的计算成本,同时线性插值方法被采用在选定聚类内生成虚拟参考点来更新数据库,最后,目标位置由声源与虚拟参考点的指纹相似度计算得出。实验结果表明,该方法能有效提高低参考点密度下的系统定位精度,同时算法复杂度低。     

A crowdsourcing-based global indoor positioning and navigation system

This paper presents three key techniques to realize a global indoor positioning system (GIPS), and a global in- and-outdoor integrated navigation system (GINS). A crowdsourcing radio map construction method, a positioning algorithm for crowdsourced radio maps, and an indoor and outdoor environment detection method are developed as the three key techniques. The developed techniques have been integrated into a crowdsourcing-based indoor positioning system, named KAILOS, aiming to realize the GIPS and GINS. The system was deployed at KAIST, Daejeon campus, and now an in- and-outdoor integrated navigation service is available at KAIST campus area. The successful launch of KAILOS foretells that the GIPS and GINS are becoming a reality.     

基于稀疏表示和位置相关性的室内定位算法

针对基于接收信号指数强度(RSSI)的WLAN室内定位算法易受干扰、波动较大及室内指纹定位方法指纹库构建繁杂而工作量较大的问题,提出了一种基于稀疏表示的指纹定位技术,在离线阶段利用压缩感知的理论来构建离线数据库,以降低离线采集的复杂度,在线定位阶段利用向量相似性理论来提高定位的精度,实验结果表明,本文提出的算法有效地提高了室内静态定位问题的精度及抗干扰性能.     

一种基于矩阵补全的室内指纹定位算法

近年来室内定位技术引起了研究者的广泛关注,现有基于信号指纹的室内定位算法需要大量采集指纹数据,且在噪声干扰下易产生较大的定位误差。针对上述问题,提出了一种鲁棒的基于矩阵补全的室内指纹定位算法,其基于信号指纹矩阵的低秩特性,将噪声干扰下的指纹数据恢复问题建模为范数正则化矩阵补全问题;在此基础上引入L1范数和F范数以平滑野值噪声并提高算法的稳定性,最终通过交替方向乘子法和变量分裂技术进行有效求解。实验结果表明,该算法只需进行少量信号指纹数据采集即可较为完整地恢复出指纹库,在各种噪声场景下均能获得高于同类算法的定位精度。     

Environmentally realistic fingerprint-image generation with evolutionary filter-bank optimization

Constructing a fingerprint database is important to evaluate the performance of an automatic fingerprint recognition system. Because of the difficulty of collecting samples, there are only few benchmark databases available. Moreover, it is hard to evaluate how robust the system is against various environments with those databases. This paper presents a novel method that generates fingerprint images automatically from only a few training samples by using the evolutionary algorithm. Fingerprints generated by the proposed method include similar characteristics of those collected from the corresponding real environment. The proposed method has been verified by comparing with real fingerprints, indicating the usefulness of the method.