基于WiFi的分级室内定位

在经典的K最近邻(K-Nearest Neighbors,KNN)的WiFi定位方法中,其算法复杂度随着定位区域和定位区域内的WiFi接入点(Access Point,AP)的增加而增加,无法满足实时定位的要求.为此,提出一种分级WiFi定位算法.算法分为粗定位和精定位阶段,首先通过AP的可见性利用汉明距离寻找可能的子区域,再用KNN算法在子区域内(利用信号强度欧氏距离)进行精定位.经过实测数据验证,平均单次定位时间在KNN算法下下降了约95％,在最大后验算法下下降了约96％,表明所提分级定位框架具有延迟低的优点.     

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。      

单WiFi室内分米级定位技术探讨

无线WiFi是现在互联网世界的基础,一切互联网技术都是在这个平台上使用的。近几年,互联网的使用很大程度上都是依赖无线网WiFi,其中的WiFi室内定位技术已经成为人们讨论最多的话题,很多人都在这方面做了研究。目前,WiFi大多数智能是在室内定位已知的先验条件下实现的,但在实际中,不能部署多个AP依然是最大问题。     

基于隐马尔可夫模型的Cell-ID定位跟踪方法

针对传统基地台识别码(Cell-ID)定位方法精确度不高的问题,提出一种新的Cell-ID定位跟踪算法。该算法利用移动台当前的服务基站信息和相邻基站信息,分别加权计算出似然向量,通过隐马尔可夫模型来获得移动台的运动状态,最后利用最大后验概率准则求出移动台的位置。实验结果表明,该算法复杂度低,在服务基站稳定的区域内定位性能良好,达到通信委员会(FCC)的定位精确度要求。     

WiFi中多AP间快速切换的研究与实现

目前,WiFi已经成为了高速无线局域网的主要标准并且得到了广泛的应用。鉴于WiFi网络接入点（AP）覆盖范围小、移动用户需要频繁切换的特点,文中提出了多AP连接和嗅探扫描两种技术来实现多AP间的快速切换。多AP连接通过同时和多个AP保持通信切实保障了通信连续性,而嗅探扫描为更有效地获取周围AP信息提供了指导性方法。实验表明这些技术有效地加快了切换的速度,有效保障了用户的QoS。     

无线局域网室内定位关键参数计算

利用无线局域网（WiFi）进行定位是室内定位的一种可靠方法,但是在对城市WiFi信号进行采样收集,以利用它进行定位时会遇到许多现实困难.首先是安全隐私问题,往往无法进入他人住宅或办公区域测量WiFi接入点（AP）的实际位置;其次是终端WiFi模块的性能问题,不同WiFi模块在同一位置对同一AP可能获得不同的信号强度,因此也会造成信号强度偏移误差,最后是传播模型中的衰减参数估计问题,复杂的环境室内中往往难以靠经验确定信号衰减参数.文中提出一种基于Keenan-Motley （KM）模型的关键参数计算方法,通过对传播模型线性化求解能够利用周边采样点有效估算AP位置、信号强度偏移误差和传播衰减参数.     

Web日志挖掘数据预处理研究

数据预处理在Web日志挖掘过程中起着至关重要的作用.论文分析了Web日志挖掘数据预处理的主要步骤,设计了用户识别、访问操作识别和路径完善三个步骤的关键算法.实验结果表明,设计的关键算法是有效的.     

多聚类融合算法在频繁非法访问检测中的应用

针对频繁非法访问的检查问题，提供了一种机器学习方法来检测登录场景中的频繁非法访问活动。通过特征工程的方法分析登录日志数据，筛选提取有效特征，再使用聚类方法对登录特征数据进行检测，分类出正常用户和异常用户。为了提高无监督识别算法的精度，提出了多聚类融合的检测算法，从多个聚类算法的角度，精确识别出登录日志中的频繁非法访问用户。实验结果证明，该方法可以更准确地提取登录场景中各项指标异常的用户，并可以扩展适应其他频繁非法访问场景。     

基于文件拆分和缓存预测的日志文件传输算法

当今的云计算平台和大型网站在运行时都会产生大量的日志文件,这些日志文件一般都具有收集分析的价值,所以在日志文件的收集过程中就出现了大日志文件的传输问题。本文要解决的问题就是如何使日志文件能够快速的传输到接收端。为此本文在研究了当前已经有的大数据传输办法之后,针对日志文件提出了与传输协议无关的新算法:文件拆分和预测算法。该算法主要由两部分组成:首先对日志文件进行拆分,拆分成包含描述性信息的文件和包含数据的文件,消除了文件中的冗余信息;然后在传输过程中通过预测接收端缓存的数据来达到消除传输过程中的冗余信息的目的。经过实验检验,本文设计的算法能够将实际传输的数据量降低90%以上。     

Web日志挖掘中预处理过程的具体研究

Web日志的数据预处理是Web日志挖掘过程中基础而关键的一步,对之后的模式识别和模式分析有着很大的影响。为了达到有效处理数据的目的,针对此预处理过程中的5个步骤逐一进行分析,并在事务识别这一步骤中,比较了常用的两种算法。最后,基于这些算法思想,在Windows平台下,采用Java语言实现了Web日志预处理。实验结果表明是有效的。     

应用于WiFi室内定位的自适应仿射传播聚类算法

在室内覆盖的大量的WiFi信号可以用来室内定位。尽管很多WiFi室内定位技术被提出,但其定位精度仍然未达到实际应用的需求。针对这个问题,该文提出一种自适应仿射传播聚类(AAPC)算法用以提高WiFi指纹的聚类质量,从而提高定位精度。AAPC算法通过动态调整参数生成不同的聚类结果,然后采用聚类有效性指标筛选出其中最佳的。采集大量真实环境数据进行试验,试验结果表明采用AAPC算法产生的聚类结果具有更高的定位精度。     

Symbol Timing Acquisition for Collaborative OFDM WLAN-Based A-GPS

Wide deployment of the 802.11g/n protocols for implementing next generation WLAN has encouraged research on the integration of these networks and GPS as a promising approach to enhance GPS for indoor positioning. WLAN, or WiFi, using the 802.11 standards, can be employed in several different ways as a complementary positioning technology for GPS navigation and the two can be used in an integrated framework to provide a continuous and robust positioning service. This paper presents receiver-level integration of 802.11g OFDM signals and GPS for a WiFi-based assisted-GPS acquisition in a multipath NLOS environment. Although previous research has been conducted to accomplish A-GPS systems using assistance information from other wireless networks (such as cellular networks), a lack of research exists to exploit 802.11 WLAN signals in order to provide complete assistance information including frequency, approximate user position and fine time assistance. Several practical time-domain OFDM timing techniques are evaluated under multipath conditions and an algorithm for relative time estimation is developed that is sufficient to enable an effective and complete WiFi-based A-GPS service. The proposed system can be deployed in places where WiFi coverage is available and where there is no or limited access to other synchronized systems. Examples include WiFi enabled mobile devices deployed on university campuses, hospitals and shopping malls, or tablet computers being used on public WiFi networks.     

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.     

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.     

Comparison of WiFi positioning on two mobile devices

Metropolitan WiFi positioning is widely used to complement GPS on mobile devices. WiFi positioning typically has very fast time-to-first-fix and can provide reliable location information when GPS signals are too weak for a position fix. Several commercial WiFi positioning systems have been developed in recent years and most newer model smart phones have the technology embedded. This study empirically determined the performance of WiFi positioning system on two different mobile devices. Skyhook's system, running on an iPhone and a laptop, was selected for this study. Field work was carried out in three cities at a total of 90 sites. The positional accuracy of WiFi positioning was found to be very similar on the two devices with no statistically significant difference between the two error distributions. This suggests that the replicability of WiFi positioning on different devices is high based on aggregate performance metrics. Median values for positional accuracy in the three study areas ranged from 43 to 92?m. These results are similar to earlier independent evaluations of Skyhook's system. The number of access points (APs) observed on the iPhone was consistently lower than that on the laptop. This lower number of APs, however, was not found to reduce positional accuracy. In general, no relationship was found between the number of APs and positional accuracy for either device, counter to earlier findings. The results indicate, however, that WiFi positioning can be achieved with a small number of APs (5–10), but that increased numbers of APs do not contribute to improved positional accuracy. Despite the agreement in aggregate performance metrics between the two devices, the replicability of WiFi positioning using Skyhook's system in terms of getting the same location by using two different devices at approximately the same place and time was relatively poor. Implications for location-based services on mobile devices are discussed.     

一种基于多天线簇的新型LTE室内定位系统

随着移动互联网业务的迅速发展,位置服务需求越来越多样化,定位技术也变得越来越重要.LTE(long term evolution)室内蜂窝网络可以解决GPS(global positioning system)信号无法进入室内的问题,从而提供高精度的位置服务.LTE室内定位需要不同基站之间进行精确同步,这给室内网络部署带来了新的挑战.针对上述问题,提出了一种基于多天线簇的LTE新型无线定位系统,通过上行探测参考信号(sounding referencesignal,SRS)获取信号到达时间差(time difference of arrival,TDOA).该方法不需要簇间同步,可有效降低网络部署难度.其次,针对此系统设计了一种新的TDOA定位算法,可支持灵活的天线簇配置.最后,通过仿真及实验验证了该定位系统及算法的有效性,实验结果表明可实现米级室内定位精度.     

低指纹密度下分段式高精确度室内定位方法

针对室内环境中传统定位方法在大定位区域、低指纹密度下定位精确度低、计算复杂度高的问题,提出了一种基于线性插值法和分布重叠的分段式定位方法。该方法采用传统的最近邻法进行粗定位,得到可信区域;利用线性插值法更新可信区域内指纹数据库,增加指纹密度;在可信区域内,采用基于分布重叠的指纹相似度匹配法实现精定位。实验结果表明,在低指纹密度下,该定位方法定位精确度较高,算法复杂度适中,具有一定的适用性。     

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.