Optimization of an RFID location identification scheme based on the neural network

An indoor localization technology is increasingly critical as location‐aware applications evolve. Researchers have proposed several indoor localization technologies. Because most of the proposed indoor localization technologies simply involve using the received signal strength indicator value of radio‐frequency identification (RFID) for indoor localization, radio‐frequency interference, and environmental factors often limit the accuracy of localization results. Therefore, this study proposes an accurate RFID localization based on the neural network (ARL‐N²), a passive RFID indoor localization scheme for identifying tag positions in a room, combining a location identification based on dynamic active RFID calibration algorithm with a backpropagation neural network (BPN). The proposed scheme composed of two phases: in the training phase, an appropriate BPN architecture is constructed using the training data derived from the coordinates of reference tags and the coordinates obtained using the localization algorithm. By contrast, the online phase involves calculating the tracking tag coordinates and using these values as BPN inputs, thereby enhancing the estimated location. A performance evaluation of the ARL‐N² schemes confirms its high localization accuracy. The proposed method can be used to locate critical objects in difficult‐to‐find areas by creating minimal errors and applying and economical technique. Copyright © 2013 John Wiley & Sons, Ltd.     

现代光通讯中的全集成CMOS限幅器及场强指示电路

针对各种光通信系统和射频通信系统中的应用,设计了一款限幅器及场强指示器(RSSI)电路,提出了一种新的直流漂移补偿方案,可使限幅器部分更加适合于全集成.对传统的RSSI电路作出了改进,大大提高了其工艺稳定性和温度稳定性.设计的限幅器具有72 dB的电压增益,可以对载波为1.5 MHz、带宽为1 MHz的中频信号进行放大;RSSI部分的动态范围为80 dBm,场强检测的误差小于±1 dB.     

基于参考标签的射频识别室内定位算法研究

针对现有基于RFID的LANDMARC室内定位系统会因外界环境因素导致优良的邻近参考标签丢失、不良的参考标签引入的问题,为此提出了一种改进的最近邻居算法,通过筛选已选取的邻近参考标签,实现最近邻标签的最佳选取。实验结果表明待测标签的定位精度得到有效提高。该算法有效获取到优良的参考标签,从而获得了满意的定位精度和定位性能。     

一种改进的ZigBee老年人定位系统设计

ZigBee是一种基于IEEE 802.15.4标准的低功耗局域网协议,具有高性能、低成本、低功耗等特点,并可以实现带有路由的组网功能。定位系统以CC2431单片机为核心,使用RSSI定位算法实现了定位功能。介绍了一种ZigBee无线定位系统,并阐述了系统的硬件组成、软件实现和定位方法。最后根据实际应用的需要对系统进行了改进,添加了老年人求助和警告功能。     

无线传感器网络RSSI测距方法与精度分析

基于RSSI的测距技术是一项低成本的距离测量技术.分析了接收信号强度指示器(RSSI)多种测距模型,结合采用IEEE802.15.4协议的CC2430芯片,设计了测距实验,获取了多组数据,通过对实验数据的分析,提出结合信标节点确定参数、高斯拟合确定测量值的RSSI测距处理方法.实验证明,该方法能提高RSSI测距的抗干扰能力,20 m内节点间的测距精度能达到1.5 m以下.     

基于信号传播特性的物理层密钥生成方案

传统基于接收信号强度的物理层密钥生成方案在窃听者靠近合法方时,合法方的密钥易被窃听者获取.针对该问题,在分析密钥误比特率的基础上,提出一种基于信号传播特性的物理层密钥生成方案.方案根据接收信号强度的实测样本估计大尺度衰落模型,提取出多径效应影响下的小尺度参数量化生成密钥.实验结果表明相比于传统方案,本方案在室内环境窃听距离大于0.6倍波长以后,窃听方密钥误比特率大于0.48;在室外环境窃听距离大于1倍波长后窃听方密钥误比特率为0.47,实现了安全可靠的物理层密钥生成.     

相关性分析在CDMA系统馈线检查中的应用

本文简单介绍了应用相关性分析对CDMA系统馈线进行检查的基本原理,结合实际的应用,验证了该方法为天馈系统的故障排查提供了一种新的便捷手段。     

基于RSS准则的CDMA软切换越区研究

硬切换技术中移动台在接收新基站信号之前中断与原基站的通信,通信中断的概率非常大。而软切换技术需要先建立与新基站之间的通信,等接收到原基站信号低于门限值时再切断与原基站的通信,故具有很高的可靠性。采用接收到的信号强度准则对软切换建立仿真模型,对两个小区之间的软切换进行了仿真,仿真结果表明,软切换的可靠性高于硬切换,采用软切换技术降低了系统的掉话率,提高了通信质量。然而,软切换也将使系统的复杂度和信道开销有所增加。     

一种基于RSS的环境自适应目标定位算法

目标定位是无线传感器网络的重要应用之一,但是基于接收信号强度(RSS)的定位方法通常因为非合作目标未知其发射功率以及不同环境下难以获取准确的路径衰减指数而无法实现准确定位,得不到广泛应用。提出了一种环境自适应的未知目标定位算法,能够实现对未知信号发射功率的目标进行准确定位,同时不断更新路径衰减指数动态适应环境,从而使提高了算法的适用性。     

基于RSSI和惯性导航的融合室内定位算法

针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。     

Comparative node selection‐based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co‐operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade‐off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering.     

基于噪声分布特性的脉冲信号自适应门限检测

通过分析数字脉冲信号幅度差分的概率分布模型,得到噪声的分布特性,并推导出用于脉冲信号检测的时域自适应门限。该门限可通过计算信号幅度差分绝对值的均值直接得到。该门限算法的第一个优点是门限值仅与噪声统计特性相关,与信号无关,可避免传统自适应算法中检测门限被强信号拉高,造成强信号附近的弱信号漏检问题;第二个优点是计算复杂度低,非常适合于实时性要求高而资源不足的现场可编程逻辑门阵列（FPGA）等硬件平台实现,为脉冲信号的实时捕获和后续的侦察处理提供支撑。     

基于BP神经网络和泰勒级数的室内定位算法研究

 在研究分析室内无线信号传播特性和传统的室内定位算法的基础上,提出了用BP神经网络来拟合室内无线信号传播模型,避免了对无线信号传播模型中参数A和n的不精确估计.在训练完成的BP神经网络的输入层输入接收信号强度值RSSI(Received Signal Strength Indicator),在输出层即可得到对应的距离值,再利用泰勒级数展开法确定盲节点的坐标位置.最终通过Matlab仿真和ZigBee平台实验验证了算法的可行性和有效性.     

一种基于RFID技术的室内定位系统设计

针对室内无线区域定位的需求,设计了基于有源RFID(Radio Frequency Identification)技术的无线定位系统。RFID读写器和标签系统均采用低功耗MCU芯片PIC16F877A作为核心控制单元,以低功耗无线射频收发器芯片CC2500为核心配合外围滤波器和天线等构成系统的通信单元。在读写器与标签进行数据通信的过程中,通过获取RSSI(Received Signal Strength Indicator,接收信号强度指示)信号推测出读写器与标签之间的距离,在获得来自于多个具有固定位置信息的标签的RSSI信号后,可以实现对读写器的无线定位。实验结果表明,该系统在室内环境中能够实现较高精度的无线区域定位。     

基于改进指纹聚类的WLAN定位优化方法

将K-means聚类算法应用到无线局域网（WLAN）位置指纹定位中,虽然可以缩短定位时间,但是容易降低定位精度。为了解决此问题,提出了基于改进指纹聚类的WLAN定位优化方法。首先根据接收信号强度标准差来优化初始聚类中心的选取,然后对指纹数据进行聚类处理,最后进行在线定位。实验结果表明,与传统的WLAN位置指纹定位方法和K-means聚类定位方法相比,基于改进指纹聚类的定位优化方法不仅缩短了定位时间,还能有效提高定位精度。     

Reliable routing mechanism based on neighbor stability for MANET

One critical issue for routing in mobile Ad-hoc networks (MANETs) is how to select a reliable path that can last longer because mobility may cause frequent breakdown in radio links. A novel routing mechanism based on link lifetime estimation is proposed in this article. In this mechanism, the node's received signal strength is obtained continuously by using Newton interpolation polynomial, and by method of middle value and interception, the reference points are selected to estimate the link lifetime. With the constraint of link lifetime and hop counts, the source nodes set up the route hop by hop. The simulations show that link lifetime can be predicted regardless of mobility pattern. Moreover, the route discovery times and packet drop ratio can be reduced by the proposed mechanism, and thus improving the performance of MANETs.     

一种基于RFID和ZigBee技术的局域定位系统

介绍了一种基于RFID和ZigBee技术的室内定位系统的设计。该设计以第二代片上系统CC2530为核心,配合RFID阅读器和标签、以及一些外围电路构成了硬件定位系统。采用基于接收信号强度值(RSSI)的定位技术和最大似然估计的计算方法进行定位。重点阐述了该定位系统的结构和硬件电路设计,分析了定位系统的工作原理、软件流程和定位算法的实现。实验证明该定位系统能够实现室内局域定位的功能。     

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.     

Indoor localization based on wireless sensor networks

Indoor localization systems are becoming very popular because they enable the creation of very interesting location-based applications. This paper provides a short introduction about localization systems based on a sensor network and the actual state of the art. Important topics related to indoor localization like the necessary infrastructure, available technologies and their expected accuracy are treated. Additionally, the results of previous work referred to the performance evaluation of localization algorithms are shortly described. Finally, some ideas related to further investigations are presented.