基于Shadowing模型的无线入侵主机物理定位研究

为了在室内无线局域网环境中及时准确地定位非法入侵主机物理位置,以无线信号强度衰减过程的Shadowing传播模型为基础,研究了在一定的范围内如何合理地布置探测器,如何根据接收到的入侵主机的信号强度进行物理定位的问题.提出的定位方法可以有效地对抗入侵主机随机更改信号发射功率对定位精度的影响.实际测试结果表明,该方法定位误差平均约为2.3 m,可以满足室内环境下定位非法入侵主机的需求.     

基于信号强度的室内定位技术

研究了基于信号强度模型的室内定位技术.通过运用线性回归、补偿式线性回归和多元回归方法,利用仿真数据建立了信号强度模型.为了理解定位误差和信号强度误差之间的关系,对这种构模方法作了分析,得出的一些重要结论有助于确定接入点(AP)的部署点及评估定位误差的范围.最后,基于IEEE802.11b MAC的典型参数进行了仿真试验.     

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.     

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RSSI的Wi-Fi室内定位常用算法对比

WiFi定位是室内定位的方法之一,其观测值主要为WiFi的信号强度(received signal strength indication,RSSI),利用信号强度进行室内定位的方法基本上可以分为两种,一种根据模型计算接收节点与发射节点的距离,估计接收节点的位置;另一种是根据事先采集的信号强度指纹进行空间匹配。通过在不同场景下对两种定位算法的比较,基于信号强度指纹匹配的定位精度明显高于基于估计接收节点的定位精度,但与指纹匹配法相比,位置估计法更加灵活。     

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。     

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.     

基于WiFi的自适应匹配预处理WKNN算法

针对基于接收信号强度（Received Signal Strength,RSS）的WiFi室内定位技术中,传统加权K邻近（Weighted K-nearest Neighbor,WKNN）算法不能自适应获取WLAN中有效接入点（Acess Point,AP）且参考点匹配准确度不高的问题,本文提出了自适应匹配预处理WKNN算法。该算法中每个实时定位点自适应地根据网络状况对AP的RSS均值由大到小排序,然后选择RSS均值较大的前M个AP,与参考点中对应的M个AP一起参与匹配预处理计算,从而优化了传统的指纹定位算法。同时将室内定位和室内地图相结合,使参考点和定位结果直观地展示在地图上,并通过使用地图数据大幅度简化了离线训练过程。此外,本文设计并实现了基于Android平台的室内定位系统,通过该系统验证了本文所提算法在单点定位和移动定位中的有效性。实验结果表明,该算法可获得30%以上的定位误差改善,有效提高了定位精度和定位稳定性。      

基于信道状态信息测距的Wi-Fi室内定位误差界分析方法

相较常用于室内定位的Wi-Fi接收信号强度(RSS),Wi-Fi信道状态信息(CSI)包含了信号传输过程中更细粒度的物理层信息(如各个子载波的幅值和相位),故可将其用于较精确的测距以实现较高的Wi-Fi室内定位精度.由于现有基于CSI测距的定位方法普遍缺少关于定位误差界的理论分析,从而导致难以对不同定位方法的理想性能进行比较.因此,该文提出一种基于CSI测距的Wi-Fi室内定位误差界分析方法,其在室内信号传播模型的基础上,考虑路径损耗、阴影衰落和多径效应与定位精度的关系,利用克拉美罗下界(CRLB)推导了时钟异步效应下基于CSI测距的定位误差界.此外,通过实验对比,分析了实际定位误差与所推导的定位误差界之间的差异,并讨论了不同实验参数对定位性能的影响.     

室内环境内的改进型Taylor级数定位算法

由于传统基于信号强度的Taylor级数定位算法是将信号强度转换为距离,会产生误差累积.在此基础上直接采用信号强度路径损耗模型,提出基于Taylor级数展开法的一种新室内定位算法,消除了误差累积;通过几何数学估计初始位置的方法解决了初始位置的选取问题.仿真试验表明,该方法避免了Taylor级数定位算法的缺陷,定位精度高,复杂度低.     

基于DS证据理论的室内移动目标RSSI定位算法

为使WiFi无线传感器网络能够利用单次获取的接收信号强度指示（Received Signal Strength Indication,RSSI）快速定位目标并减小RSSI阴影衰落对定位的影响,提出一种改进的基于Dempster-Shafer（DS）证据理论的利用RSSI信息对室内移动目标定位（Locating Indoor Mobile Target with RSSI based on DS evidence theory,LIMT-DS）的方法。LIMT-DS方法根据传感器接收到的RSSI值构造关于目标位置估计的条件概率密度函数,并据此通过改进的证据构造方法生成各传感器关于定位环境中位置点的证据,对各位置点进行证据综合,最后通过改进的决策模式选择出目标存在可能性较大的数个定位点进行位置加权,获得目标的位置估计。仿真与实验结果表明,LIMT-DS方法可以用传感器网络单次获得的RSSI信息实现对目标的定位,其定位性能明显优于同类方法。      

Location determination using WiFi fingerprinting versus WiFi trilateration

Many applications in the area of location-based services and personal navigation require nowadays the location determination of a user not only in an outdoor environment but also an indoor. Typical applications of location-based services (LBS) mainly in outdoor environments are fleet management, travel aids, location identification, emergency services and vehicle navigation. LBS applications can be further extended if reliable and reasonably accurate three-dimensional positional information of a mobile device can be determined seamlessly in both indoor and outdoor environments. Current geolocation methods for LBS may be classified as GNSS-based, cellular network-based or their combinations. GNSS-based methods rely very much on the satellite visibility and the receiver-satellite geometry. This can be very problematic in dense high-rise urban environments and when transferring to an indoor environment. Especially, in cities with many high-rise buildings, the urban canyon will greatly affect the reception of the GNSS signals. Moreover, positioning in the indoor/outdoor transition areas would experience signal quality and signal reception problems, if GNSS systems alone are employed. The authors have proposed the integration of GNSS with wireless positioning techniques such as WiFi and UWB. In the case of WiFi positioning, the so-called fingerprinting method based on WiFi signal strength observations is usually employed. In this article, the underlying technology is briefly reviewed, followed by an investigation of two WiFi-positioning systems. Testing of the system is performed in two localisation test beds, one at the Vienna University of Technology and another one at the Hong Kong Polytechnic University. The first test showed that the trajectory of a moving user could be obtained with a standard deviation of about ±3–5 m. The main disadvantage of WiFi fingerprinting, however, is the required time consuming and costly signal strength system calibration in the beginning. Therefore, the authors have investigated if the measured signal strength values can be converted to the corresponding range to the access point. A new approach for this conversion is presented and analysed in typical test scenarios.     

基于多元高斯混合模型的离线指纹数据库

针对室内环境下单次采样测量值的波动变化及信号间的相互干扰,该文提出一种基于分区多元高斯混合模型(MVGMM)的室内定位系统。根据信号接入点(AP)铺设位置与空间结构,系统采用一对多支持向量机算法对目标区域做分区操作,以精确信号变化的区域范围。利用狭小分区内信号间的耦合关系,建立基于信号间相互干扰的多元高斯混合模型,以改善信号波动所造成的定位精度下降。当室内环境发生变化时,基于分区多元高斯混合模型的自适应更新算法可对各分区指纹数据的可信度做出判断,并以自适应算法更新信号波动较大分区的模型参数,提高模型与现有环境间的耦合程度。实验结果表明,该文算法可利用相对少量样本数据,构建稳定可维护的室内信号分布模型,相较于其他算法,其定位精度也有一定程度提高。     

采用距离无偏估计的加权最小二乘定位算法

随着WiFi网络的广泛覆盖,基于接收信号强度的定位技术成为研究热点。针对已有基于接收信号强度定位算法定位性能不高的实际问题,提出一种基于距离无偏估计的加权最小二乘定位算法。该方法首先利用接收信号强度观测模型计算得到信号源与传感器之间距离的无偏估计量,然后根据距离计算公式建立方程组;接着把距离的无偏估计量代入方程组得到关于信号源位置的线性最小二乘模型,同时计算线性最小二乘模型中的噪声协方差矩阵;最后运用加权最小二乘方法计算得到信号源位置的估计量。该文对所提算法进行了充分的计算机仿真,仿真结果表明:在不同的定位环境下,所提算法的定位性能均优于传统加权最小二乘算法和最佳线性无偏估计算法。      

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。     

Heterogeneous Cooperative Localization for Social Networks

Location-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are based on ranging techniques, they are highly dependent on the distance interpreted from the received signal strength (RSS) or time of arrival from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: centroid scheme, nearest neighbor scheme, kernel scheme and AP density scheme to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and city downtown. Moreover, we compared our results with the WiFi positioning system made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density scheme and kernel scheme outperform than other schemes.     

稀疏参考点下的室内定位方法

随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进行了深入研究。本文改进了卡尔曼滤波有效解决了Wi-Fi的噪声和缺失点,设计了基于信号强度差分方差的无线接入点筛选策略来滤除信息量较低的接入点,提出了一种基于支持向量回归拟合的克里金插值算法（Kriging Interpolation Algorithm Based On Support Vector Regression, SVR-Kriging）进行指纹库的构建,最后通过接入点加权的K加权近邻法（AP weighted and Weighted K-Nearest Neighbor, AWKNN）完成定位。将该方法应用于实际的二维、三维定位场景,实验结果表明二维场景平均定位误差为1.01 m,三维场景平均定位误差为0.92 m。该方法解决了指纹数据库信号采集困难、接入点数据冗余的问题,有效地降低了定位误差。      

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.     

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.