基于RSSI的无线传感网络协同定位算法及其应用

为了解决传统无线传感网络(WSN)中定位方法精度不高的问题,将协同定位的技术应用到无线传感网络的定位中。首先,通过对盲节点与参考节点的距离关系的分析,初步确定了盲节点区域;然后利用接收信号强度指示(RSSI)测距技术建立了盲节点之间的相对位置关系,并应用盲节点的相对位置关系多次迭代缩小盲节点区域,精确盲节点位置;在此基础上,提出了基于RSSI的无线传感网络协同定位算法。在Zigbee平台上对该算法进行了技术评价,与传统定位方法进行了对比实验,实验结果表明,基于RSSI的无线传感网络协同定位算法具有一定的精度优势,特别是当参考节点数目较少时,这种优势比较明显,具有良好的应用前景。     

一种用于家庭牧场的RSSI定位算法

针对家庭牧场对羊的定位需求,提出了一种用于草原环境的RSSI定位算法.该算法采用噪声模型处理原始数据,结合质心算法进行目标定位及路径距离计算.实验结果表明,该算法可较准确地计算出牧场内羊的位置坐标及活动的路径距离.     

基于高斯混合模型的非视距定位算法简

针对无线传感器网络室内节点定位,在分析定位误差模型的基础上,结合高斯混合模型提出了一种无需先验知识的节点定位算法。利用高斯混合模型,对含有非视距误差的距离测量信息进行训练,以获得接近真实值的距离估计值。为取得高精度的定位效果,采用粒子群算法对期望最大化 (EM)算法进行优化。同时结合优选残差加权算法对所得的距离值进行定位估计,得出目标节点坐标估计值。仿真实验结果证实了算法的有效性。     

WLAN射频接收端RSSI电路的设计

设计了一种基于分段线性对数限幅放大器和基于非平衡对全波整流器的接收信号强度指示器(Received Signal Strength Indicator,RSSI)。RSSI连接在WLAN接收端的LPF之后,用于检测经过滤波后的信号强度。该RSSI设计基于SMIC 55nm CMOS工艺,采用7级限幅放大器级联,每级增益为10 dB。RSSI输入动态范围为60 dB,输出直流电压范围为0.5~2.0 V,斜率为22.5 mV/dB。结果显示,RSSI在各个corner和温度下的误差仅为±0.5 dBm,版图面积为480μm×160μm,功耗为8 mW。     

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.     

Environmental-adaptive indoor radio path loss model for wireless sensor networks localization

In the received signal strength indicator (RSSI) based indoor wireless sensor networks localization system, RSSI measurements are very susceptible to multipath fading, anisotropy of antenna, low supply voltage of node and so on, which will cause the system failure to achieve a high location accuracy. This paper presents an environmental-adaptive path loss model. In the process of localization, the calibrated coefficient LSV of low supply voltage, which can be determined by monitoring the supply voltage of the sender, is used to calibrate ranging errors caused by its low supply voltage. The blind node utilizes the absolute value of RSSI to generate the phase of the corresponding receiver's location so as to determine the correction coefficient of indoor multipath fading R_i. Furthermore, in order to improve the accuracy of RSSI measurements, we also take full consideration of the effect of antenna to accurately determine the corresponding path loss model of the two communication nodes. The proposed path loss model is suitable for the majority of wireless location systems that are on the basis of RSSI-based ranging techniques. Experiment results show that the estimation accuracy and adaptability of the proposed path loss model are significantly higher than that of the traditional one.     

基于传感网络的居家养老系统设计与实现

随着我国老龄化人口越来越多,社会集中养老难于满足中国现状。本文基于传感网络,构建出以社区居家养老为主体的新型养老监护系统。该系统以ZigBee平台为基础,通过传感器节点对监护对象生命体征数据的采集,以STC89C51为控制器,以CC1101和CC2530为数据收发模块,完成数据传输。实验测试表明,平台具有良好的数据的采集与传输能力,并且具有良好的扩展性,具有一定的应用性。     

基于RSSI梯度的AP定位算法(英)

Recent rapid rise of indoor location based services for smartphones has further increased the importance of precise localization of Wi-Fi Access Point(AP).However,most existing AP localization algorithms either exhibit high errors or need specialized hardware in practical scenarios.In this paper,we propose a novel RSSI gradient-based AP localization algorithm.It consists of the following three major steps:firstly,it uses the local received signal strength variations to estimate the direction(minus gradient) of AP,then employs a direction clustering method to identify and filter measurement outliers,and finally adopts triangulation method to localize AP with the selected gradient directions.Experimental results demonstrate that the average localization error of our proposed algorithm is less than 2meters,far outperforming that of the weighted centroid approach.     

研究片内多径分离技术在基于RSSI定位中的应用

无线传感器网络中基于接收信号强度指示(RSSI)的定位易受多径干扰。传统的基于RSSI定位方法主要通过增加测距样本来抵抗多径干扰,而在物理层研究抗多径干扰的较少。该文将片内多径分离技术应用于基于RSSI的定位中,通过抽取每个码片的第1个采样点进行相关解扩得到分离多径后的直达径,并求其功率得到抗多径干扰的RSSI值。理论分析其多径的分离能力,信噪比损失情况。最后通过仿真比较了传统方法与该文方法求得的RSSI值方差,该文方法在多径信道下的方差更小。     

煤矿安全监测无线传感器网络节点定位技术

利用无线信号强度实现了煤矿安全监测无线传感器网络节点间的自定位,提出了提高定位精度的改进方案,并分析了不同因素对定位精度的影响．仿真结果表明,所提出的算法可以有效地提高无线传感器节点的定位精度,如在路径损耗标准偏差分别为4和6,滚动平均次数为10时,定位精度的相对误差分别从0.21和0.35左右下降到0.18和0.29左右.