Adaptive Empirical Path Loss Prediction Models for Indoor WLAN

This paper presents robust empirical path loss models to characterize indoor propagation for access point (AP) deployed at different heights. The proposed models are developed with wireless local area network infrastructure at 2.4 GHz. The models are backed by extensive received signal strength (RSS) measurements acquired in line of sight and obstructed line of sight regions. The models are developed for two conditions, viz; quasi realistic and realistic RSS measurements. The quasi realistic measurements are taken after suppressing human intervention and electrical interferences to minimum. While the realistic RSS measurements are made in presence of all the human interventions and electrical interferences. The shadow fading component for both quasi realistic and realistic conditions is statistically modeled with the dependency on AP height. The proposed technique can be applied with higher confidence level to the buildings with similar construction features where RSS measurements are made upon. The results reveal that the performance of the proposed propagation models is significantly higher than the existing International Telecommunication Union-path loss model. The results also demonstrate that the realistic path loss model is more robust than the quasi realistic model.     

室内WLAN定位中位置指纹优化的接入点部署方法

提出了一种新的室内WLAN定位中指纹优化的接入点（AP）部署方法。首先在最大化不同位置指纹信号强度间欧式距离的基础上构造注水优化模型,然后经过多次迭代的过零调整及离散调整处理,得到各AP候选位置的非负离散权重,最后根据最大平均权重准则,合理部署AP进而优化位置指纹。实验表明,所提方法能够获得较高的位置指纹定位精度及较低的AP优化位置搜索时间开销。     

A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network

In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also the coordinates of the anchor points (APs). The localization accuracy depends on the errors in the distance estimation with the RSS measurements and the size of the polygon composed of the APs used for the lateration. Since errors are inevitably involved in the RSS measurement, we focus on reducing the size of the polygon to increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon composed of the APs hearing the target. Once the relative position is estimated, only the APs covering the area are used for localization. We implement the localization method and evaluate the accuracy of the proposed method in various radio propagation environments. The experimental results show that the proposed method improves the localization accuracy and is robust against the dynamically changing radio propagation environments over time.     

Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.     

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.     

Analysis of hyperbolic and circular positioning algorithms using stationary signal-strength-difference measurements in wireless communications

Referring received signal strength (RSS) to a signal propagation model to find user location is one of the most promising strategies in wireless communications. This paper develops a simple method based on relative signal-strength measurements, that is, the differences in stationary signal strength measured at the user location from multiple base transceiver stations (BTSs). The stationary signal strength is the averaged RSS and also is a stationary Gaussian process. In using this method, it is vitally important to confirm that some uncertain propagation parameters can be canceled out while a signal propagation model is merged into our method for locating users. In this way, the differences in stationary signal strength lead to two solutions: One is the distance difference between pairs of BTSs, and the other is the distances from the user location to the multiple BTSs. Consequently, the hyperbolic positioning algorithm due to the distance-difference solution and the circular positioning algorithm due to the distances solution can be presented, respectively. Afterward, some experimental results were drawn from a field trial in a real propagation environment. Results show that the hyperbolic and circular positioning algorithms can locate the user to within about 350 and 300 m in 67 percentile, respectively. Compared with the numerical result reported in the literature on existing methods based on RSS only, our method is superior. Despite the result not meeting Federal Communications Commission (FCC) requirements, this method proved to be sufficiently simple and efficient in terms of the computation at burden and network signaling load.     

Data fusion of power and time measurements for mobile terminal location

The location of mobile terminals in cellular networks is an important problem with applications in resource allocation, location sensitive browsing, and emergency communications. Finding cost effective location estimation techniques that are robust to non-line of sight (NLOS) propagation, quantization, and measurement noise is a key problem in this area. Quantized time difference of arrival (TDoA) and received signal strength (RSS) measurements can be made simultaneously by CDMA cellular networks at low cost. The different sources of errors for each measurement type cause RSS and TDoA measurements to contain independent information about mobile terminal location. This paper applies data fusion to combine the information of RSS and TDoA measurements to calculate a superior location estimate. Nonparametric estimation methods, that are robust to variations of measurement noise and quantization, are employed to calculate the location estimates. It is shown how the data fusion location estimators are robust, provide lower error than the estimators based on the individual measurements, and have low implementation cost.     

Indoor/outdoor location of cellular handsets based on received signal strength

The feasibility of received signal strength (RSS) methods for locating handset calls in a complicated wireless cellular environment is demonstrated. An extensive measurement campaign conducted on the Georgia Tech campus indicates that RSS location techniques can locate handset calls within 100 m error distance to its ground truth 78% of the time for a network with a majority of indoor users. A received signal strength aggregate (RSSA) method for identifying and locating indoor handsets is also proposed.     

Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in outdoors cannot work well in indoors. Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also we assign lower weights to altering APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs.

     

An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy

In recent years, the indoor positioning technologies have been recognized as core technologies for realizing smart space, a ubiquitous society, context awareness, and various location-based services. There are several approaches for positioning with radio signals, but the received signal strength (RSS)-based technology is considered a promising scheme because of its simplicity and practicality in implementation. In this paper, the positioning performance of the RSS value-based scheme is analyzed with respect to the location of access points (APs) and the number of APs in an indoor environment. An adaptive AP selection scheme and a base AP changing scheme are then proposed to enhance the positioning accuracy. In order to estimate the RSS characteristics, RSS values are measured as the distance between the AP and the receiver increases. The positioning performance is evaluated with differing AP numbers, which form a triangle or a quadrilateral. The performance of the proposed schemes is evaluated via experiments using wireless local area network APs. Results show that the performance of proposed schemes is enhanced compared to that of conventional scheme.     

On the accuracy of a cellular location system based on RSS measurements

We discuss the performance of a cellular location system based on received signal strength (RSS) measurements. Each mobile station (MS) collects RSS measurements of the downlink control channels transmitted by the surrounding base stations. It is assumed that there is one-to-one mapping between the RSS and the MS location. Hence, these measurements can be used to obtain the MS location. We examine the accuracy of this method by deriving the Cramer-Rao bound, the concentration ellipse, and the circular error probability (CEP) of this method. In addition, we obtain an analytic expression that predicts the point at which accuracy deviates significantly from the bound (the threshold point). The accuracy of the method does not meet the FCC E911 requirement, but it is an attractive solution for less-demanding location-based services.     

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。      

RSS协助的ray-tracing室内定位算法

针对室内定位,当信号受到非视距(non-line-of-sight, NLOS)和多径传播的影响时,本文提出一种接收信号强度(Received Signal Strength, RSS)协助的Ray-tracing室内定位算法,改进已经提出的基于虚拟基站方法的信号到达时间 (Time of Arrival, TOA)和信号到达角度(Direction of Arrival, DOA)室内无线信号Ray-tracing模型,利用信号RSS测量值优化算法,实现TOA、DOA和RSS协同定位,提高室内多径及非视距环境下,无线定位的精度,降低算法复杂度,提高算法处理信号多重散射的能力并降低了对基站的依赖性适用环境更为广泛。首先通过RSS得到信号源可能存在的位置,随后利用Ray-tracing原理并使用虚拟基站,将非视距路径定位问题转化为视距路径定位问题,利用TOA和DOA对直射、透射、反射和绕射情况进行分析建模,最后使用最小二乘法对可能的位置进行筛选,得到信号源的最终位置。仿真结果表明,本算法较改进前拥有更高的定位精度。      

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.     

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods     

Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure

With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoretical base and develop a localization algorithm for building a zero-configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our departmental building, Siebel Center for Computer Science. The empirical results show the proposed system is quite robust and gives accurate localization results.     

基于遗传算法优化BP神经网络的可见光定位

采用接收信号强度(RSS)方法的室内可见光定位 ,因受多径效应及噪声的影响,对距离估计不准确, 定位精度不高。为提高定位精度,本文提出了一种采用遗传算法优化BP神经网络(GA-BP) 的距离估计方法。 先通过遗传算法优化BP神经网络的初始权值,经过优化后的BP神经网络收敛速度快,不易 限于局部最优。 再利用GA-BP神经网络对收发端之间的距离进行修正,使其接近于真实距离。最后使用最 小二乘法解算待 定位点坐标,同时在不同定位范围和不同定位位置下,与传统RSS加权质心方法的可见光定 位结果进行对 比。仿真结果表明,在5m×5m×3m的定位场景中,平均定位误差可以达到0.642 cm。与传统RSS加权质 心方法相比,平均定位精度提高了约96.4%。且在不同定位范围和不 同定位位置下,平均定位误差稳定在 毫米级,尤其不随定位范围的扩大而扩大。有效地提高了室内定位精度和系统应用的普适性 。     

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.     

Research on indoor localization algorithm based on kernel principal component analysis

An indoor localization algorithm based on kernel principal component analysis (KPCA) was proposed.It applied KPCA to train the original location fingerprint (OLF) and extract the nonlinear feature of the OLF data at the offline stage,such that the information of all AP was more efficiently utilized.At the online stage,an improved weight k-nearest neighbor algorithm for positioning which could automatically choose neighbors was proposed.The experiments were carried out in a realistic WLAN environment.The results show that the algorithm outperforms the existing methods in terms of the mean error and localization accuracy.Moreover,it requires less times of RSS acquisition and AP number.     

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。