Interference Mitigation in a Repeater and Pseudolite Indoor Positioning System

The widespread use of the personal navigation devices makes indoor positioning a major technological issue. The development perspectives of location-based services dramatically increase the importance of developing adequate solutions. In this paper, we carry out a theoretical study of an indoor positioning technique based on time-delayed Global Navigation Satellite Systems (GNSS) repeaters. It is a simple solution deploying minimal infrastructure which can use either outdoor repeated Global Positioning System (GPS) signals or a single signal generator. However, the positioning method presents limitations in terms of correlation and tracking performances. The paper presents theoretical approaches in order to overcome the interference problems and to improve the quality of the GPS signal reception. The new system based on ldquorepealitesrdquo (that comes from repeater and pseudolite) makes the best use of repeater and pseudolites in order to allow a fair continuity of the GNSS service indoors.     

无线局域网室内定位关键参数计算

利用无线局域网（WiFi）进行定位是室内定位的一种可靠方法,但是在对城市WiFi信号进行采样收集,以利用它进行定位时会遇到许多现实困难.首先是安全隐私问题,往往无法进入他人住宅或办公区域测量WiFi接入点（AP）的实际位置;其次是终端WiFi模块的性能问题,不同WiFi模块在同一位置对同一AP可能获得不同的信号强度,因此也会造成信号强度偏移误差,最后是传播模型中的衰减参数估计问题,复杂的环境室内中往往难以靠经验确定信号衰减参数.文中提出一种基于Keenan-Motley （KM）模型的关键参数计算方法,通过对传播模型线性化求解能够利用周边采样点有效估算AP位置、信号强度偏移误差和传播衰减参数.     

On the inclusion of geographic information systems (GIS) in global navigation satellite systems (GNSS)

The latest innovation of the global navigation satellite systems (GNSS) technologies plays an important role in improving the quality and safety of modern life. Most of the applications evolved from the integration between GNSS, geographical information systems (GIS) and wireless communications and networking (WCN) systems. The wide spread applications that are using these technologies include: the automatic vehicle location (AVL), tracking systems, navigation systems, pedestrian navigation systems, intelligent transportation systems, precise positioning, and emergency callers, among others. The location‐based services (LBS) are possible only by the combination of GNSS, GIS and WCN. The growing need for commercial LBS has forced cellular‐phone and network manufacturers to concentrate on positioning solutions, which are even more precise than the regulatory mandates for positioning of emergency callers and other user services and applications. In this paper, we will present a literature review of the GNSS, the three satellite systems GPS, GLONASS and Galileo, which are aimed to support GNSS services, and a comparison between them and their role in creating a GIS. Copyright © 2006 John Wiley & Sons, Ltd.     

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 an...     

Sensor data fusion for an indoor and outdoor localization

Geolocation systems are constantly evolving to enhance the integrity, accuracy and availability. Today, the applications are emerging and multiplying which are parts of an overall context of mobility. In outdoor environments, GNSS systems used, such as GPS and Galileo, provide a good accuracy, but in the indoor environments, GNSS signal is deteriorated due to the signal degradation by different obstacles. Many techniques are used to locate users in the indoor environments such as Infrared, Ultrasound or Radiofrequency techniques. The use of these techniques facilitates the exchange and dissemination of information. This paper presents a new design of Indoor-Outdoor positioning system based on the combination of data from UWB and GPS sources.     

面向移动LBS的智能手机室内定位技术探讨

移动互联网的发展给基于位置的服务(LBS)带来了广泛的应用前景,智能手机的普及使用户定位和获取LBS变得更加方便。目前,手机定位技术特别是室内定位,仍不够成熟,已经成为制约LBS业务发展的技术瓶颈。本文在研究定位技术现状和发展趋势的基础上,提出一种新的室内定位方法,依靠智能手机内置传感器进行图像特征识别和运动状态检测,即可完成室内环境的准确定位,具有成本低、简单方便、灵活易扩展的优点,能够满足移动LBS业务模式发展需求。     

多距融合室内定位算法研究

目前WiFi在室内环境中使用频次高,用户在通过两个相邻AP时会发生AP切换,并累计产生大量WiFi访问日志。WiFi日志中包含定位所需的接收信号强度指示符,在定位系统中直接利用WiFi访问日志中的数据,将极大地简化定位部署复杂度。文中提出了一种在两个相邻AP环境下,基于WiFi日志的多距融合室内定位算法,并通过实验仿真将新算法与路径损耗模型定位方法进行对比。实验结果表明,新算法具有易部署、低成本、低复杂度等特点,当训练样本个数达到300时即可达到稳定定位效果。     

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.     

Comparison of WiFi positioning on two mobile devices

Metropolitan WiFi positioning is widely used to complement GPS on mobile devices. WiFi positioning typically has very fast time-to-first-fix and can provide reliable location information when GPS signals are too weak for a position fix. Several commercial WiFi positioning systems have been developed in recent years and most newer model smart phones have the technology embedded. This study empirically determined the performance of WiFi positioning system on two different mobile devices. Skyhook's system, running on an iPhone and a laptop, was selected for this study. Field work was carried out in three cities at a total of 90 sites. The positional accuracy of WiFi positioning was found to be very similar on the two devices with no statistically significant difference between the two error distributions. This suggests that the replicability of WiFi positioning on different devices is high based on aggregate performance metrics. Median values for positional accuracy in the three study areas ranged from 43 to 92?m. These results are similar to earlier independent evaluations of Skyhook's system. The number of access points (APs) observed on the iPhone was consistently lower than that on the laptop. This lower number of APs, however, was not found to reduce positional accuracy. In general, no relationship was found between the number of APs and positional accuracy for either device, counter to earlier findings. The results indicate, however, that WiFi positioning can be achieved with a small number of APs (5–10), but that increased numbers of APs do not contribute to improved positional accuracy. Despite the agreement in aggregate performance metrics between the two devices, the replicability of WiFi positioning using Skyhook's system in terms of getting the same location by using two different devices at approximately the same place and time was relatively poor. Implications for location-based services on mobile devices are discussed.     

M2M-Kommunikation und Satellitennavigation

Nowadays, it is impossible to imagine daily life without satellite communication and satellite navigation. Many applications and services use communication and satellite navigation technologies in order to facilitate daily life. This article focuses on location-based services (LBS) provided by TeleConsult Austria which can be used in different fields and circumstances. These services contribute significantly to personal security and increased mobility.     

应用于WiFi室内定位的自适应仿射传播聚类算法

在室内覆盖的大量的WiFi信号可以用来室内定位。尽管很多WiFi室内定位技术被提出,但其定位精度仍然未达到实际应用的需求。针对这个问题,该文提出一种自适应仿射传播聚类(AAPC)算法用以提高WiFi指纹的聚类质量,从而提高定位精度。AAPC算法通过动态调整参数生成不同的聚类结果,然后采用聚类有效性指标筛选出其中最佳的。采集大量真实环境数据进行试验,试验结果表明采用AAPC算法产生的聚类结果具有更高的定位精度。     

基于WiFi的室内定位技术在博物馆的应用

在室内定位技术迅速发展的时代,由于WiFi技术的传输速度快、运行稳定、传输流量大等特点,因此在博物馆的展示导览服务领域内可以将WiFi技术与智能终端相结合,弥补传统人工讲解服务所存在的不足,既满足了观众需要,又有利于博物馆对展品的保护与管理。本文通过一个WiFi室内定位系统的实践应用展示了基于该技术在博物馆应用的可行性和有效性。     

Impact of Galileo commercial service on location-based service providers: business model analysis and policy implications

Today's mobile location-based services (LBSs) largely depend on a free-of-charge, best-effort positioning technology, called global positioning system, which is controlled by the US military. The European alternative Galileo will not only offer a similar best-effort system by 2020, but also a premium-rate service known as Galileo commercial service (CS). Galileo CS is planned to provide higher positioning accuracy, improved security due to signal authentication and service guarantee. While the technology behind Galileo is often studied, the impact of Galileo CS on the LBS marketplace is rarely discussed. In this article, we fill this gap by analysing how improved accuracy, authentication and service guarantee may impact the business models of LBS providers. We do so by interviewing service providers, policy makers and industry experts on what new services would be enabled; technological alternatives that may emerge in the coming years; and organisational and financial issues that service providers face when adopting such a premium-priced positioning signal. We find that a more accurate, secure and reliable global navigation satellite system signal enables a range of new LBSs, although several alternative technologies are emerging that may make Galileo CS obsolete before it is even launched. To convince the LBS providers to adopt Galileo CS, the institution operating Galileo should get governments on board early on for building trust and should consider progressive pricing schemes. Still, service providers are sceptical about adopting Galileo CS, and the hope to recoup any investments in Galileo may thus be in vain.     

基于一次反射的室内WiFi辐射源单站定位方法

为了解决WiFi外辐射源雷达现场应用中首先需要实时获取室内辐射源位置的问题,通过详细分析室内环境中信号的反射方式和特性,利用一次反射信号建立了WiFi辐射源测量的到达时间差(TDOA)模型,并分析推导出辐射源坐标求解方程。对于该非线性方程,先利用Taylor展开在初值处将其线性化,然后利用高斯牛顿迭代法估计辐射源坐标,且具有较快的收敛速度。仿真分析表明其所提算法可实现室内辐射源定位,且因方程线性化带来的精确度损失可通过迭代得到快速补偿。     

Intelligent location models for next generation location-based services

To become truly ubiquitous, next generation location-based services (LBS) will have to rely on mobile platforms upon which multiple sensors and measurement systems have been integrated to provide continuous, three-dimensional positioning and orientation. Such technologies are explored today for example in mobile mapping systems, vehicle navigation systems and mobile robot navigation. Next-generation LBS also need theoretically sound methods to translate position into location information. The article addresses this problem: the transformation of position into meaningful and reliable location, and the transformation of location knowledge into positioning constraints. It suggests by this way an intelligent location model that integrates sensor fusion with spatial knowledge fusion via a feedback cycle. It is shown that this feedback cycle consists of three layers: spatial constraints, temporal constraints and spatiotemporal constraints.     

基于WiFi的分级室内定位

在经典的K最近邻(K-Nearest Neighbors,KNN)的WiFi定位方法中,其算法复杂度随着定位区域和定位区域内的WiFi接入点(Access Point,AP)的增加而增加,无法满足实时定位的要求.为此,提出一种分级WiFi定位算法.算法分为粗定位和精定位阶段,首先通过AP的可见性利用汉明距离寻找可能的子区域,再用KNN算法在子区域内(利用信号强度欧氏距离)进行精定位.经过实测数据验证,平均单次定位时间在KNN算法下下降了约95％,在最大后验算法下下降了约96％,表明所提分级定位框架具有延迟低的优点.     

一种导航新技术:协作机会导航

现实环境下,包含多种无线电信号,当GNSS系统使用受到限制时,利用周围射频环境中的机会信号提取定位和授时信息,通过多个机会导航接收机共享信息,构建并不断完善所需信号的整体框架,形成了被称为协作机会导航的新型导航体制。本文介绍了协作机会导航的产生背景和基本思想,从环境可观测性角度,讨论了协作机会导航的实现方法,并对协作机会导航系统的应用前景进行了展望。     

基于WiFi的目标反射信号提取及定位算法

近年来,利用WiFi的普遍性与其提供的信道信息对覆盖范围内的人物进行定位成为一个十分热门的研究方向。现存的室内定位通常需要提前在离线阶段建立相应特征库,有些系统还对设备有特殊要求。文章提出了一种基于商用WiFi路由器和无线网卡的系统,通过对信道状态信息(CSI)进行处理,成功分离出由移动目标反射的动态路径信号,用于参数提取,构建定位模型,对目标进行定位跟踪,可以做到0.7 m精度的定位。     

单WiFi室内分米级定位技术探讨

无线WiFi是现在互联网世界的基础,一切互联网技术都是在这个平台上使用的.近几年,互联网的使用很大程度上都是依赖无线网WiFi,其中的WiFi室内定位技术已经成为人们讨论最多的话题,很多人都在这方面做了研究.目前,WiFi大多数智能是在室内定位已知的先验条件下实现的,但在实际中,不能部署多个AP依然是最大问题.     

基于WiFi信号的多维参数估计与三维轨迹跟踪算法

在未来物联城市的规划和现有的发展情况下,基于位置的服务变得越来越重要。室内定位在很多场景都有着明朗的应用前景,如轨迹跟踪、老人看护、手势识别等。但是现存的室内定位通常需要在离线阶段大量实验以建立相应特征库,有些系统还对设备有特殊要求。另外,现有的定位系统多是基于对二维平面上的运动进行定位,未曾考虑对进行三维运动的人进行定位。对此我们提出了一种基于商用WiFi路由器和无线网卡下的系统,对人体反射回的无线信号做处理,从而得到多普勒频移、信号飞行时间、到达角度这些多维参数,对进行三维运动的人进行跟踪定位。我们的系统只用两个接收端,可以动态的适应环境,提取出多维用以估计位置的参数,达到0.7m精度的三维轨迹跟踪。