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.     

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。      

基于多LED的高精度室内可见光定位方法

针对可见光室内定位问题,该文基于接收信号强度(RSS)定位技术,提出一种利用多个LED发射端实现室内定位的方法,即MLED-RSS定位算法。该方法在充分考虑LED拓扑结构对定位性能影响的基础上,利用部署在室内的多个LED,合理选择其中3个LED作为发射节点,采用改进的三边定位法获得定位目标位置信息。定位算法可以有效地解决可见光定位存在的遮挡效应。仿真实验表明,MLED-RSS算法可以实现高定位精度。     

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.     

Pre-scan based fast handoff scheme for enterprise IEEE 802.11 networks

In IEEE 802.11 networks, many access points (APs) are required to cover a large area due to the limited coverage range of APs, and frequent handoffs may occur while a station (STA) is moving in an area covered by several APs. However, traditional handoff mechanisms employed at STAs introduce a few hundred milliseconds delay, which is far longer than what can be tolerated by some multimedia streams such as voice over Internet protocol (VoIP), it is a challenging issue for supporting seamless handoff service in IEEE 802.11 networks. In this paper, we propose a pre-scan based fast handoff scheme within an IEEE 802.11 enterprise wireless local area network (EWLAN) environment. The proposed scheme can help STA obtain the best alternative AP in advance after the pre-scan process, and when the handoff is actually triggered, STA can perform the authentication and reassociation process toward the alternative AP directly. Furthermore, we adopt Kalman filter to minimize the fluctuation of received signal strength (RSS), thus reducing the unnecessary pre-scan process and handoffs. We performed simulations to evaluate performance, and the simulation results show that the proposed scheme can effectively reduce the handoff delay.     

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.

     

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     

利用现有无线局域网进行室内定位算法研究

目前,多种WiFI室内定位方案被提出,但是往往需要重新部署无线AP,造成成本和复杂度上升。本文充分利用现有无线局域网的拓扑结构进行室内定位研究,提出了一种自适应网络变化的WKNN指纹算法,该算法通过实时监控无线AP的匹配数,自动根据位置适应网络变化,定位精度明显提高。在此基础上,为了减少无线信号不稳定引起的定位误差,提出了一种新的数据修正方法,该方法根据移动平均速度动态预测标准,动态调整a参数将预测坐标与实测坐标加权,从而得到最终定位坐标。最后,算法在实际环境中验证表明,利用现有无线局域网的自适应网络算法和数据修正使定位获得了33.5%的误差改善。      

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.     

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.     

Green wireless local area network received signal strength dimensionality reduction and indoor localization based on fingerprint algorithm

Green wireless local area network (WLAN) is an emerging technology to achieve both the purposes of power conservation and high‐speed accessing to the Internet because of the working on‐demand strategy adoption and high density access points (APs) deployment. Although it is good news to data traffic service, Green WLAN brings severe challenges to the indoor localization service based on fingerprint algorithm. Redundant APs will greatly enlarge the radio map and introduce a much heavier computation burden to the terminal for localization in the online phase. In addition, APs in Green WLAN are powered on and off to make balances between data traffic service demand and energy saving goals so that the received signal strength (RSS) sampled online and recorded in the radio map offline are rarely matched in the same detected AP number, which leads to asymmetric matching problem occurring in the fingerprint algorithm. In this paper, we propose to make a nonlinear dimensionality reduction on the RSS by local discriminant embedding algorithm to realize both the computation burden decreasing and asymmetric matching problem resolving for the fingerprint algorithm in Green WLAN. The simulation results show that our proposed methods could effectively reduce the computation burden in the online phase and make the fingerprint algorithm operate more robustly when the RSS is reduced to the intrinsic dimensionality in Green WLAN. Copyright © 2013 John Wiley & Sons, Ltd.     

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。     

基于朗伯模型参数估计的VLC室内定位优化算法

为实现高精度室内定位,本文设计了一种可见光 通信(VLC)室内定位系统,并通过 结合优化的朗伯模型、码分多址技术(CDMA)、三边定位算法而有效提升了定位精度和系统 扩展性。首先,每个发光二极管(LED)的ID信息经过直接序列调制后加载到LED驱动电路上 ,LED发出带有自身ID信息的灯光信号。在接收端通过光电探测器(PD)接收灯光信号,并 根据扩频码的正交性恢复出ID信息及接收信号强度(RSS),以此提高信道容量并增强系统 抗干扰能力。然后,根据朗伯光源模型,由三边定位算法得出待定位点的定位估计坐标。为 进一步提高精度,引入k最近邻(KNN)思想,采集适当的指纹点并由指纹点信息对每盏灯在 定位估计坐标处的朗伯光源模型参数进行估计,由优化后的朗伯模型计算出精度更高的定位 坐标。在1m×1m×1.35 m的空间区域中,进行本VLC室内定位系统 的实验测试。结果表明,提 出的高精度VLC室内定位系统的平均定位误差降低至2cm左右,其定位精度相比于传统三边 定 位算法提升了30%。此外,该系统方案所采用基于指纹点信息优化朗 伯模型参数的方法具备良好的实用扩展性,可实现广阔的应用场景。     

位置指纹定位技术

在室内利用无线网络对移动目标定位,可用的技术有基于波达时间(TOA/TDOA)、基于波达角度(AOA)、以及基于接收信号强度(RSS)的技术等。比较而言,基于接收信号强度(RSS)的位置指纹(LF)技术更适合于复杂的室内环境。对该技术目前的研究情况进行比较全面和详细的介绍。     

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.     

A Novel Wireless Positioning System for Seamless Internet Connectivity based on the WLAN Infrastructure

Nowadays, several positioning systems are available for outdoor localization, such as the global positioning system (GPS), assisted GPS (A-GPS), and other systems working on cellular networks, for example, time difference of arrival (TDOA), angle of arrival (AOA) and enhanced observed time difference of arrival (E-OTD). However, with the increasing use of mobile computing devices and an expansion of wireless local area networks (WLANs), there is a growing interest in indoor wireless positioning systems based on the WLAN infrastructure. Wireless positioning systems (WPS) based on this infrastructure can be used for indoor localization to determine the position of mobile users. In this paper, we present a novel wireless positioning system, based on the IEEE 802.11b standard, using a novel access point (AP) with two transceivers to improve the performance of WPS in terms of accuracy of the location estimation and to avoid service connectivity interruption. In our proposed system, the novel AP uses the second transceiver to find information from neighboring mobile stations (STAs) in the transmission range and then sends information in advance to associated APs, which estimate the location of the STA based on an internal database. We also use a TDOA technique to estimate the location of the STA when there is not enough information in the database (in this case, the STA moves into a new area where the system has not run the calibration phase). Using TDOA, the database can be generated and updated automatically. The initial results from our simulations show that the proposed system provides higher accuracy of location estimation than other related work and does not interrupt the Internet connection for end users in contrast with other proposed schemes.

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.     

Adaptive access points deployment for Indoor bluetooth positioning accuracy enhancement

The indoor positioning system based on fingerprint receives more and more attention due to its high positioning accuracy and time efficiency. In the existing positioning approaches, much consideration is given to the positioning accuracy improvement by using the angle of signal, but the optimization of access points (APs) deployment is ignored. In this circumstance, an adaptive APs deployment approach is proposed. First of all, the criterion of reference points (RPs) effective coverage is proposed, and the number of deployed APs in target environment is obtained by using the region partition algorithm and full coverage algorithm. Secondly, the wireless signal propagation model is established for target environment, and meanwhile based on the initial APs deployment, the simulation fingerprint database is constructed for the sake of establishing the discrimination function with respect to fingerprint database. Thirdly, the greedy algorithm is applied to optimize APs deployment. Finally, the extensive experiments show that the proposed approach is capable of achieving adaptive APs deployment as well as improving positioning accuracy.     

Landmark‐based device calibration and region‐based modeling for RSS‐based localization

During the past decades, many fingerprint‐based indoor positioning systems have been proposed and have achieved great success. However, uncontrolled effects of device diversity, signal noise, and dynamic obstacles could recognizably degrade the performance of modern fingerprint‐based indoor localization systems. In this paper, to amend the variations in radio signal strengths (RSSs) caused by device diversity, we proposed an automatic device calibration process. Because of device diversity, the sensed RSS would deviate from the trained radio map and thus leads to poor positioning. An RSS transform function could be adopted to calibrate the RSS variation between different devices and overcome the device diversity problem. However, to train the transform function, a data collection process is required. Unlike conventional calibration methods requiring manual data collection, we proposed a landmark‐based automatic collection process. Based on the detection of Wi‐Fi landmarks, our system could automatically collect pair‐wise RSS samples between devices and train the RSS transform function without extra human power. In addition, to well represent the effects of signal noise and dynamic obstacles, a region‐based RSS modeling method was also proposed. The proposed modeling method allows our system to perform region‐based target localization and utilize more robust region information for localization. Experiments in various environments demonstrate that our system could give a better positioning performance by properly handling the RSS variation caused by signal noise, dynamic environment, and device diversity. Copyright © 2015 John Wiley & Sons, Ltd.