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为了解决室内密集多径环境下人员定位精度差这一问题,设计并实现了一种基于脉冲超宽带与能量检测接收机的信号到达时间估计算法。根据在IEEE802.12.4a标准室内家居环境下得到的超宽带信号,深入分析了信号的偏度、翘度与均方差特性。根据信号特性与信噪比之间的关系,算法能够实现高精度的信号传播时间估计。该算法充分考虑了积分周期与信号传播环境如视距与非视距对信号传播时间造成的影响。通过实验分析对比,该算法对信号传播时间估计具有更好的鲁棒性,目标运动速度和形变具有很好的鲁棒性,能够提供更高的精度。相较于阈值估计算法,该算法在低信噪比环境下最优能够提高10 ns的估计精度。 相似文献
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超宽带(Ultra Wide Band, UWB)技术是一种新兴的无线载波通信技术,其具有发射信号功率谱密度低、系统复杂度低、定位精度高等优势,尤其适用于像电厂等密集多径场所的高速无线连接,但在传输过程中信号会被环境中的各种因素影响,进而会影响室内定位的精度。基于此,针对室内非视距(Non Line of Sight, NLOS)环境下,提出一种非视距混合滤波加权算法,能够有效对测距数据进行平滑处理,进而降低异常值的影响,再利用时间差定位法(Time Difference of Arrival,TDOA)测量方法,在原始Chan算法的基础上提出一种改进的Chan定位算法,解决NLOS误差引起的定位信息不准确的问题,最终实现更精准的TDOA定位。仿真实验证明,所提算法在室内NLOS环境中具有更高的定位精度。 相似文献
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本文阐述的是一种针对室内超宽带系统(UWB)的时间差到达和角度到达(TDOA/AOA)的混合定位技术。由于非视距传播(NLOS)误差确定为此系统的主要误差原因,所以本文使用卡尔曼滤波器来甄别和消除非视距误差,从而减小在室内UWB环境下的NLOS的时间到达(TOA)误差。本文加入了一种AOA选择功能。最后针对使用TDOA和有选择的AOA的室内移动定位追踪系统本文提出了一种改进的扩展卡尔曼滤波器(EKF)。仿真结果显示本文提出的混合定位方案可以有效响应在UWB环境下的NLOS/LOS变化,并且提高了定位精度。 相似文献
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陈思翰 《太赫兹科学与电子信息学报》2017,15(5):752-755
传统基于全球定位系统(GPS)的定位技术应用,已在室外环境下为人们提供了许多便利。随着近年来人们生活水平的不断提高,大众对定位的诉求已不仅限于室外,在智慧商场、企业人员智能管理、校园智能管理等场景,要求对室内用户进行定位监测,而传统的GPS定位精确度有限,在室内已无法应用。目前室内定位常用的技术有红外线、蓝牙、Wi-Fi以及基于室内移动网路的无线定位等,无线定位则是其中的热点。本文介绍了一种基于室内分布式基站,运用Fang算法实现到达时间差(TDOA)定位的技术研究,并对其定位精确度进行了探索。 相似文献
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文中讨论了定位估计采用的几种算法-LSE(Least Sqllare Error)、APIT及Taylor算法,并提出新的算法,即将APIT和Taylor结合起来进行协同定位的方法.将几种算法运用于超宽带定位中,并讨论平均定位误差与噪声方差、参考节点数及定位失败率等之间的关系,对APIT、Taylor及两者的协同算法的累计分布函数(CDF)进行了仿真. 相似文献
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针对现阶段基于双边双向测距(double sided two-way ranging,DS-TWR)算法的超宽带(ultra wide band,UWB)室内定位系统存在通信次数较多、多标签环境下冲突率较高的问题,提出了一种改进的算法。该方法通过Hash算法对标签和基站的通信内容进行哈希分布,使得基站在每次测距流程中,能够对多个标签进行有规则的统一回复,大大减少了基站发送RES(responds)数据的次数。结果表明,改进算法后,单个标签和基站的通信次数较传统DS-TWR算法减少了15%,增加了基站接收状态在测距中的时间占比,由此降低了基站在接收RNG(range)数据包的冲突率,测距成功率提高了43.6%。由于每个定位周期内所需要通信次数的减少且数据包之间冲突率的降低,将需要更小的信道容量,由此增加了定位系统的标签容纳量,具有较强的工程意义。 相似文献
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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。 相似文献
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消防员在地下建筑、无窗建筑、大型建筑物内,执行火情控制与人员搜救时,不可避免会遇到浓烟、黑暗、高温、陌生等情况,因此就需要精确的室内定位系统支持,已有的室内定位技术无法满足定位精度和长期部署的需求.本文提出了一种基于无源射频识别(RFID)标签的消防员室内定位系统,通过在消防员头盔内安装可变功率的RFID读写器,并将建筑物内读取的已经过精确标定的RFID标签信息回传到远程服务器进行计算处理,可实现建筑物外指挥员对室内消防员的实时精准定位和指挥功能. 相似文献
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《Journal of Location Based Services》2013,7(1):22-37
During the last 10 years, many modern IT-based applications have developed inside buildings. Many of those applications would benefit by the ability to locate people and/or objects inside the building (indoor positioning). However, most of today's indoor positioning systems are not able to deliver precise position information (<10?cm) along with quality parameters. Ultra wide band (UWB) is a new radio-based technology that allows the determination of distances in indoor environments with a very high spatial resolution even through building materials. At the Institute of Geodesy of TU Darmstadt, a high-resolution UWB positioning system (UWB-ILPS; ILPS, indoor local positioning systems) based on trilateration principle has been developed to estimate the position of a mobile station precisely. To benefit from knowing the position and orientation, it is necessary to select and merge data linked to the user's location for indoor location services. By this means, the visitor to a public building may benefit from the system as his position is shown on a digital floor plan generated dynamically or by retrieving location-based information inside the building. Mixed reality systems also offer advantages for a mobile building information system. For this purpose, a webcam was replaced by the digital camera in the UWB-ILPS prototype. Knowing the camera's location in space and its view direction, one is able to merge the real world taken by the webcam with the virtual world represented by a 3D CAD model of the building. 相似文献
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针对于LANDMARC算法的RFID室内定位精度受传输路径影响严重,直接采用粒子滤波自适应性差的问题,提出一种基于改进粒子滤波的RFID室内定位算法.该算法首先利用极限学习机(ELM)拟合阅读器接收信号强度与标签距离之间的非线性关系,构建信号传输模型,筛选邻近标签集;然后采用自适应学习因子优化粒子滤波过程,提高粒子全局... 相似文献
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ABSTRACT Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends. 相似文献