利用智能手机传感器建立室内定位指纹库的wifi指纹定位方法

无线电定位系统在室外空间已经取得了巨大成功,GPS等卫星定位系统的已广泛应用,科技界在室内定位技术方面虽然出了很多成果,但都没有取得广泛应用,本文介绍了定位技术的基本原理,无线电定位研究的热门方法.分析了当前几种热门方法的优缺点,并针对wifi信号强度指纹定位方法的缺点提出了一种弥补的方案.     

室内定位系统中位置指纹库滤波算法研究

WiFi指纹定位是目前最受欢迎的室内定位技术之一,离线阶段创建的位置指纹库的精确与否对定位的精准度有很大的影响。传统的指纹库构建一般对采集的数据进行均值滤波,误差较大,并且在范围较大的场合进行定位时,由于位置指纹数据过多,使得定位时效性不好,结果不理想。为了提高指纹库的可靠性,通过给采集的源指纹数据赋予权值并根据权值的大小划分有效数据进行滤波处理,建立高精度指纹库,并利用基于临近域加权最近邻算法进行验证。实验结果表明,与传统的构建指纹库和定位算法相比,该方法显著提高了定位精度,缩短了定位时间。     

一种基于位置指纹的室内WLAN定位方法

针对室内信号时变性导致定位不准的问题,提出了一种改进的3阶段位置指纹定位法。采样阶段,将采集信号的坐标、方位、接收信号强度的高斯分布及其对应的无线接入点等信息存储在数据库中生成位置指纹;在校正阶段中,利用参考点间信号强度的关联性信息,使用局部加权线性回归法,计算出一些虚拟点的信号强度;最后是线上实时定位阶段。通过与传统的加权K最邻近算法、直方图和联合聚类等3种定位方法相比较,该算法在同样的场景下可以取得更好的定位精度。     

改进指纹强度下的WiFi室内定位算法

目前的室内定位算法利用信号传播模型求出信号强度后直接进行求解定位,使得定位误差较大.为提高定位精度,提出利用信号能量的欧氏距离方法进行加权,然后对改进后的信号强度进行阈值滤波加均值滤波双重处理.定位阶段,在K最近邻算法基础上利用距离的倒数作为加权函数的算法进行定位.经仿真结果表明,改进后的算法相比于一些典型的定位算法,定位精度有较大提高.     

基于RSS相关性的位置指纹室内定位方法

解决设备差异性造成的Wi-Fi信号强度不确定问题是位置指纹室内定位应用与推广的关键.一种基于设备间接收信号强度(Received Signal Strength,RSS)相关性的位置指纹室内定位方法被提出.以智能手机为用户终端,离线阶段,通过智能手机扫描的Wi-Fi信号强度信息,经过数据处理,筛选稳定的接入点(Access Point,AP),构建离线指纹数据库;在线定位阶段,对于实时获取的Wi-Fi信号强度信息,进行筛选处理后,挑选与离线指纹共同拥有的AP,并根据该AP集合,形成新的离线指纹和在线指纹.对离线指纹按RSS的大小降序排序;在线指纹,则以同一次序对RSS排序,然后利用皮尔逊相关系数和杰卡德相似系数,计算指纹相似度并排序,通过K最近邻(K-Nearest Neighbor,KNN)算法实现用户定位.实验表明该方法可有效解决设备差异性问题,并实现精确定位,平均定位误差达到1.7 m.     

室内位置指纹定位方法中的小波去噪应用

采集的位置指纹包含的大量随机噪声严重影响着位置指纹定位方法的精确度,为了提高位置指纹定位方法的精确度,文中提出了一种基于卡尔曼滤波和小波去噪相结合的方法来去除采集获得的位置指纹中的随机噪声的方法。实验结果表明,与常规的去噪方法相比,基于此方法的位置指纹定位算法精度有一定程度的提高。     

An adaptive location estimator using tracking algorithms for indoor WLANs

This paper presents adaptive algorithms for estimating the location of a mobile terminal (MT) based on radio propagation modeling (RPM), Kalman filtering (KF), and radio-frequency identification (RFID) assisting for indoor wireless local area networks (WLANs). The location of the MT of the extended KF positioning algorithm is extracted from the constant-speed trajectory and the radio propagation model. The observation information of the KF tracker is extracted from the empirical and RPM positioning methods. Specifically, a sensor-assisted method employs an RFID system to adapt the sequential selection cluster algorithm. As compared with the empirical method, not only can the RPM algorithm reduce the number of training data points and perform on-line calibration in the signal space, but the RPM and KF algorithms can alleviate the problem of aliasing. In addition, the KF tracker with the RFID-assisted scheme can calibrate the location estimation and improve the corner effect. Experimental results demonstrate that the proposed location-tracking algorithm using KF with the RFID-assisted scheme can achieve a high degree of location accuracy (i.e., more than 90% of the estimated positions have error distances of less than 2.1 m).     

Sequential learning for fingerprint based indoor localization

In this paper, we introduce a novel approach for improving performance of fingerprinting based indoor localization. Our proposal is a two-step procedure in which severe variation in the received signal strength is minimized during the first step via convex optimization, and distance metric learning is then used to estimate a more accurate location. Numerical results show that our proposal outperforms existing techniques in terms of accuracy and reliability.     

Extreme learning machine for indoor location fingerprinting

With the rapid growing market of wireless devices, positioning systems that make use of the signal strength of wireless devices are gaining more interest nowadays. Being able to track the location of a Wi-Fi or Radio Frequency Identification device could improve the quality of services in various sectors, including security, warehouse, logistic management, and healthcare. As compared with outdoor environment, positioning systems face a greater challenge in indoor environment because wireless signal is significantly influenced by building layout and surrounding objects, for which a location fingerprinting approach is needed. Moreover, the signal strength of a wireless device may also change over time, which is known as temporal variation, and therefore a reliable location estimation system must have the ability to learn and adapt with temporal changes. However, if the learning process is highly complex and requires long processing time, deploying the system into a larger scale would not be feasible. In recent years, Extreme Learning Machine (ELM) has surfaced as a viable alternative that challenged the norm of iterative and progressive learning. ELM has also been considered as a solution for indoor location fingerprinting. However, there has not been a comprehensive review on how the ELM-based approaches are linked with existing location fingerprinting techniques. Here we discuss some major location fingerprinting techniques, which are nearest-neighbor, LANDMARC, and LEMT, and formulate a new framework for systematically translating the techniques into ELM-based methods.     

Broadband ultrasonic location systems for improved indoor positioning

Ultrasonic location systems are a popular solution for the provision of fine-grained indoor positioning data. Applications include enhanced routing for wireless networks, computer-aided navigation, and location-sensitive device behavior. However, current ultrasonic location systems suffer from limitations due to their use of narrowband transducers, This paper investigates the use of broadband ultrasound for indoor positioning systems. Broadband ultrasonic transmitter and receiver units have been developed and characterized. The utilization of these units to construct two positioning systems with different architectures serves to highlight and affirm the concrete, practical benefits of broadband ultrasound for locating people and devices indoors.     

Methods for optimizing the location of base stations for indoor wireless communications

When designing wireless communication systems, it is very important to know the optimum locations for the base station antennas. In this paper, a model has been developed to set up an optimization problem, the solution of which provides the information for the optimum location of the base station antennas particularly for an indoor environment. Several methods for the optimization of the cost function are presented and the final results are compared with each other. This methodology can be applied for the design and planning of the location of base station antennas for indoor wireless communication systems. Two numerical examples have been presented to illustrate the application of this methodology.     

与强度无关的位置指纹在线定位改进算法

将现有的研究较热的固定发射端、定位接收端的室内位置指纹定位系统应用到固定接收端、定位发射端的室内无线电干扰源定位中,研究了一种考虑干扰源发射强度的在线定位算法,以克服直接使用以一固定发射强度信号源建立的指纹数据库和现有的在线定位方法无法实现对不同发射强度干扰源定位问题。在分析信号传播模型和现有在线定位方法的基础上,利用强度差的均方差进行定位计算。给出了算法实现依据和流程,对改进的算法进行了仿真和实验验证,结果证明了改进算法的实用性。同时,改进算法的思想对现有的考虑接收端异质的位置指纹定位系统也有一定的指导意义。     

Characterising indoor positioning estimation using experimental data from an active RFID-based real-time location system

Indoor positioning has attracted much research effort due to many potential applications such as human or object tracking and inventory management. Whilst there are a number of indoor positioning techniques and algorithms developed to improve positioning estimation, there is still no systematic way to characterise the estimation. In this paper, we propose a method comprising of three characteristics to characterise indoor positioning estimation. We conducted experiments on an active radio frequency identification (RFID)-based real-time location system in different environmental conditions. We used both a human and a robot to traverse two experimental areas and collected positioning results at different fixed points along the traversal path. Using this basic positioning data, we were able to characterise positioning estimation using three characterisations: position accuracy, centroid consistency and angular distribution. We demonstrate the use of these characteristics for examining different points in a travelling path and different measurements.     

5G超密集网络下的室内压缩重构指纹定位算法

为减少室内定位复杂度并进一步提高定位精度,提出了一种5G超密集网络下的室内压缩重构指纹定位算法.该算法分为离线建库阶段和在线匹配阶段两个阶段.离线建库阶段采用了矩阵填充理论进行指纹库的构建,只需采取少量的指纹点构建具有低秩特性的局部指纹库,并通过非精确增广拉格朗日乘子法(Inexact Augmented Lagrangian Multiplier Method,IALM)算法进行矩阵填充,从而恢复完整的指纹库.在线匹配阶段采用卡方距离代替传统的欧式距离来计算待定位点与参考指纹点的相似度,并用加权K近邻算法估算出待定位点坐标.经过实验仿真分析,所提算法以1.13％的误差节约了40％的工作量,在信噪比为10 dB时定位误差最小为0.2008 m,与传统K近邻指纹匹配算法相比具有更好的定位精度.     

基于Kalman滤波器的室内激光/INS融合定位方法

讨论未知多边形室内环境下微型飞行器(MAV)的定位问题。利用惯性测量单元(IMU)测量飞行器运动参数,并计算飞行器的位置、航向。二维激光测距仪测量飞行器相对于障碍的位置,用迭代搜索/最小二乘算法匹配扫描数据,获得2次扫描的相对位移和转动。然后分析激光扫描匹配和惯性导航系统(INS)的误差,建立相应误差模型,并以此设计间接反馈校正Kalman滤波器。仿真结果显示,该激光/INS融合定位方法相比纯INS定位和无融合激光扫描匹配定位的精确度更高,并能将误差限制在一个较小的范围内。     

一种室内无线网络多用户自适应定位及跟踪算法

室内移动网络中各用户的路由策略和其所在的位置紧密相关,所以为提供高效的路由方案,首先要实现准确的室内定位。同时,某些特定的部门,如警方、军方和消防等,更加迫切的需要实用的室内定位算法。但是由于室内环境复杂性的影响,手机和各接收机的距离估计可能存在严重误差。针对复杂的室内环境,提出了一种利用测量到用户之间的接收功率实现定位及跟踪的算法。仿真和模拟证明该方法是有效的。     

Fast and resource efficient method for indoor localization based on fingerprint with varied scales

To improve the prediction speed in indoor localization,a novel algorithm based on fingerprint with varied scales was proposed.It divided the region of interest into distinct zones with distinctive coverage indicators,and reference positions with different distribution density were set in the region.According the time relevance and strength vary of the RSS from the anchors,the grids-matching process was greatly sped up for the usage of coverage indictors and the features of the location fingerprint extracted with the PCA,which made the proposed method fit the demand of application with limited power and memory.Experimental results indicate that accuracy of the positioning is ensured with the reduced energy-consuming,and more flexible about the number of anchors and the grid distribution.     

A survey of indoor positioning systems for wireless personal networks

Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.     

Software tool for optimising indoor/outdoor coverage in aconstruction site

A system architecture, an empirical propagation model, and a software combined with a CAD tool, have been designed to offer mobile communication services to construction sites. Results have been validated by measurements