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。      

基于多个传感器的VLC室内定位算法研究

为了实现移动机器人的快速高精度定位,提出了一种基于多个传感器的室内定位模型,研究了其可见光通信技术(VLC)室内定位算法,并对该算法进行了实验验证。首先研究基于AOA定位算法,利用传感器的响应曲线,结合室内定位模型,通过拟合预测算法计算出信号到达角度实现定位;然后综合多个传感器的定位模型和AOA定位算法,分析得出一种室内定位的实现方式,通过实验验证了该定位模型和定位算法的实现可行性。结果表明:其定位精度达13.6cm,定位周期为0.1s,相较于传统的AOA定位算法,该算法定位精度高、成本低、可行性高且定位速度快。     

基于双麦克风声源定位的视频跟踪

声源定位跟踪技术在当今社会有着越来越广泛的应用。在此使用两个高灵敏度麦克风作为传感器,配以音频信号处理芯片,接收音频信号并进行模数转换,使用FPGA器件作为核心控制器,结合TDOA算法和ILD算法,实现在室内环境下、二维平面内的声源定位。并根据声源定位的信息驱动摄像头转动,使其一直对准于声源所在位置,并保持持续跟踪。FPGA使用NiosII内核,方便使用高级语言进行程序设计。摄像头的视频输出信号可接于PC机或其他视频设备。与其他定位算法相比。系统减少了使用传感器的数量。     

A new method for improving Wi-Fi-based indoor positioning accuracy

Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.     

基于Wi-Fi和基站信号强度的室内定位系统设计与实现

分析了现有的室内定位技术,对室内定位信号测量技术、位置估算技术、定位采用的信号以及典型的室内定位系统作了详细阐述。提出了基于Wi-Fi和基站信号强度的室内定位系统架构,并从功能实现和软件架构2个方面说明了该系统的组成,最后经实验证明该系统能够实现及时稳定的室内定位和位置服务。     

Environment Adaptive Localization Method Using Wi-Fi and Bluetooth Low Energy

Received signal strength indicator (RSSI) based fingerprinting techniques for indoor positioning can be readily implemented via a wireless access point. These methods have therefore been widely studied in the field of positioning. However, fingerprinting suffers low accuracy of positioning on account of high noise occurrences which are caused by other wireless communication signals and environmental factors when the RSSI is received, and by relatively high errors on account of low position resolution compared to other methods such as time of flight and inertial navigation technology. In this paper, a modified fingerprint algorithm based on Wi-Fi and Bluetooth low energy applied to the log-distance path loss model is proposed to remove unnecessary Wi-Fi data, and produce the AP database that can be updated depending on the changes of the ambient environment as the indoor area is increasingly complicated and extended. Instead of using the existing fingerprinting techniques of consulting signal strengths as factors that are stored in a database, the proposed algorithm employs environmental variables to which the log-distance path loss model is applied. Therefore, the proposed algorithm has higher position resolution than existing fingerprint and can improve the accuracy of positioning because of its low dependence on reference points. To minimize database and eliminate inaccurate AP signals, the Hausdorff distance algorithm and median filter are applied. Using a database in which environment variables are stored, the results are inversely transformed into the log-distance path loss model for expression as coordinates. The proposed algorithm was compared with existing fingerprinting methods. The experimental results demonstrated the reduction of positioning improvement by 0.695 m from 2.758 to 2.063 m.     

Wireless fingerprinting indoor positioning using affinity propagation clustering methods

According to rapid extension of wireless sensor network localization, indoor localization using fingerprint has turned out to be more considerable lately. It contains of a database called Receive Strength Signal Indicator vectors, which is a primitive amount in wireless sensor network fingerprinting positioning. The equivalence of a few strategies is brought up from the literary works, and some new variants are presented in this study. A combination of a clustering strategy named affinity propagation and statistical and probabilistic positioning procedures is considered in this review and at the same time, the impact of some components in our methodology onto positioning precision will be investigated. Affinity propagation clustering method set up a common baseline for assessing the relative accuracy of various indoor location methods effectively. Eventually two coarse localization methods as Mahalanobis norm method and similarity to exemplar receive strength signal vector are compared based on positioning accuracy and performance. Experimental outcomes prove that the intended algorithm will advance the accuracy and localization error compared with the method without clustering.     

基于改进接收信号强度指示的室内定位算法

为了提高室内环境节点定位精度,针对传统定位算法的不足,提出一种改进接收信号强度指示的室内定位算法。首先通过神经网络对各锚节点接收信号强度的权值进行拟合,得到路径损耗模型的参数值,然后利用最大似然法对未知节点进行定位,最后采用仿真实验测试其性能。结果表明,相对其它室内定位算法,本文算法提高了室内定位的精度,降低了平均定位误差,可以满足室内定位的实时性要求。     

An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks

This paper presents a hybrid localization algorithm for wireless sensor networks (WSNs) that simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme are also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. To this end, two different types of estimators based on Taylor-series (TS) expansion and maximum-likelihood (ML) estimation are first proposed to solve the set of nonlinear RSS/TDOA equations taking into account measurement errors. The corresponding Cramér-Rao lower bound (CRLB) for the established scheme is then derived and utilized as a performance measure for the two estimators. Simulation results show that the proposed hybrid positioning approach significantly outperforms the previously considered localization solutions in WSNs, thanks to the joint process of the received signals’ power and time difference of arrival. The advantages of the proposed scheme in providing high location accuracy, fast convergence, low complexity implementation, and low power consumption make it an attractive localization solution via WSNs.     

Scene analysis indoor positioning enhancements

The emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one.     

基于信号分布混合假设检验的Wi-Fi室内定位方法

Wi-Fi室内定位技术是目前移动计算领域的研究热点之一,而传统位置指纹定位方法没有考虑复杂室内环境下Wi-Fi信号分布的多样性问题,从而导致Wi-Fi室内定位系统的鲁棒性较差。为了解决这一问题,该文提出一种基于信号分布混合假设检验的Wi-Fi室内定位方法。首先根据Jarque-Bera(JB)检验结果对各个参考点处的Wi-Fi信号分布进行正态性评价;然后针对不同Wi-Fi信号分布特性,利用混合Mann-Whitney U检验/T检验方法构造匹配参考点集合,以实现对目标的区域定位;最后通过计算定位区域中匹配参考点的K近邻(K-Nearest Neighbor, KNN),完成对目标的位置坐标估计。实验结果表明,所提方法相比于传统Wi-Fi室内定位方法具有更高的定位精度和更强的系统鲁棒性。     

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RSS的室内可见光差分修正定位算法

可见光通信技术是利用白光LED同时实现照明和通信的新型通信技术,为室内定位技术提供了新的可能.针对可见光通信中接收信号强度RSS随机波动较大的问题,提出一种基于RSS的改进的差分修正定位算法,通过将各个参考节点分别作为差分修正参考节点进行定位修正,规避了设定参考节点权重过大的问题.该定位算法有效地提高了室内定位精度,无需增加额外硬件设备,计算量小,容易实现.仿真结果表明,该定位算法在5m×5m×3m的空间区域中能够实现约15 cm的平均定位误差性能.     

二值传感器网络的分布式稀疏LMS算法

在基于无线传感器网络的参数估计中,每个节点在数据采集、存储、处理和传输等方面的能力是有限的。二值传感器网络中的每个节点只能提供低精度1比特测量值,与能够提供模拟测量值（无限精度）的传感器相比,二值传感器有较低的使用成本。如何利用低成本二值传感器网络获得较好的参数估计性能近些年已引起广泛关注,基于该二值传感器网络,论文提出了一种分布式稀疏参数估计的自适应最小均方（LMS）算法。该算法采用稀疏惩罚最大似然优化,并结合期望最大化和LMS方法,获得稀疏信号的在线估计。仿真实验表明,尽管只采用1比特测量,提出的算法仍具有较好的收敛性,并且稳定状态的估计误差接近于非1比特测量的同类算法。      

A Novel Energy-Aware Target Tracking Method by Reducing Active Nodes in Wireless Sensor Networks

Energy consumption is one of the main challenges in wireless sensor networks. Additionally, in target tracking algorithms, it is expected to have a longer lifetime for the network, when a better prediction algorithm is employed, since it activates fewer sensors in the network. Most target tracking methods activate a large number of nodes in sensor networks. This paper proposes a new tracking algorithm reducing the number of active nodes in both positioning and tracking by predicting the target deployment area in the next time interval according to some factors including the previous location of the target, the current speed and acceleration of the target without reducing the tracking performance. The proposed algorithm activates the sensor nodes available in the target area by predicting the target position in the next time interval. The problem of target loss is also considered and solved in the proposed tracking algorithm. In the numerical analysis, the number of nodes involved in target tracking, energy consumption and the network lifetime are compared with other tracking algorithms to show superiority of the proposed algorithm.     

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。      

一种基于脉冲压缩的漏缆传感器定位新方法

漏缆传感器的入侵探测定位系统已广泛应用于国防设施、银行、监狱等重要区域,为提高这种系统的定位精度,提出了一种基于脉冲压缩的漏缆传感器探测定位新方法。该方法将巴克码调制的二进制相移键控信号引入系统作为发射信号,并对接收信号进行滤波相减、相干解调、抽样判决和自相关运算等一系列信号处理,以获得含有入侵位置信息的特征信号。应用MATLAB 和电磁仿真软件CST 对该系统进行了协同仿真分析,并搭建了实验系统进行测试。仿真结果与实测结果吻合良好,验证了新方法探测定位的有效性。新方法的定位精度约为0.9 m,优于目前已报道的同类探测定位系统的定位精度。     

基于RSSI加权质心的光纤光栅传感网络冲击定位

针对材料结构损伤位置识别的精确定位问题,通过构建分布式光纤布拉格光栅(FBG)传感网络,利用光纤光栅传感器的传感特性,根据感知的冲击响应信号强度(RSSI)以及冲击点到传感器距离的关系,提出一种基于RSSI加权质心的光纤光栅传感网络冲击载荷定位方法。设计合理的传感器网络监测布局,通过分析不同位置传感器感知的冲击响应信号强度辨识冲击点所在的区域,采用加权质心定位算法对冲击载荷的位置识别定位。试验表明:分别构建基于碳纤维复合材料结构板、钢板、木板损伤识别模式的定位监测实验系统,在300mm×300mm的监测区域内随机选取24个冲击点进行位置识别,能准确辨识所有实验冲击点所在的区域,并根据RSSI来确定冲击点的位置坐标,坐标定位的平均误差在15mm以内,可实现对冲击点位置的识别,为准确识别材料结构的损伤位置提供了一种实用可行的方法。     

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.     

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.