Kernel-based particle filtering for indoor tracking in WLANs

Indoor localization using signal strength in wireless local area networks (WLANs) is becoming increasingly prevalent in today’s pervasive computing applications. In this paper, we propose an indoor tracking algorithm under the Bayesian filtering and machine learning framework. The main idea is to apply a graph-based particle filter to track a person’s location on an indoor floor map, and to utilize the machine learning method to approximate the likelihood of an observation at various locations based on the calibration data. Nadaraya–Watson kernel regression is adopted to interpolate the Received Signal Strength (RSS) distribution for nonsurvey points. The success of the proposed kernel-based particle filter (KBPF) lies in the fact that KBPF incorporates the environmental and motion constraints into the model and restricts particles to propagate on the graph which precludes the locations that the person is unlikely to be at, and that the developed nonlinear interpolation method is effective in inferring the RSS distribution for the non-survey location points which makes it possible to reduce the total number of survey locations. In addition, missing value problem is addressed in this paper, and different methods are compared through experiments. We conducted a series of experiments in a typical office environment. Results show that KBPF achieves superior performance than other existing algorithms. It even yields higher accuracy with only a small fraction of training data than others with a full training data set. As a consequence, by applying KBPF, sub-meter accuracy can be obtained while extensive calibration effort can be greatly reduced. Although KBPF is more computationally complex, it can still provide real time estimates.     

基于Meanshift和粒子滤波的行人目标跟踪方法

针对粒子滤波跟踪算法计算代价大以及Meanshift跟踪算法容易陷入局部极值等问题,提出一种嵌入均值优化的粒子滤波跟踪算法。该算法根据粒子滤波的运动模型估计目标区域位置,利用Bhattacharyya距离度量粒子区域和目标模型的相似性,并根据相似性来更新粒子权值,使用Meanshift优化算法改善粒子的估计位置,使得这些粒子的候选区域能更加接近目标模板,极大提高了粒子的使用效率。实验结果表明,该算法能够有效进行人的跟踪,处理人的短暂遮挡问题,性能优于粒子滤波算法,有较好的实用性。     

结合Hash函数和密钥阵列的RFID安全认证协议

无线传输、信号广播、资源受限等特点使无线射频识别(RFID)技术存在潜在的安全隐患。在分析目前常见的RFID认证协议基础上,提出一种结合单向Hash函数和密钥阵列的安全认证协议。新协议在认证过程中使用单向Hash函数值代替标签标识符ID,并为每一对阅读器和标签之间设计独立的认证密钥,在抵抗包括窃听、位置跟踪、重传攻击、拒绝服务和篡改等多种攻击的基础上,进一步增强了标签信息的私密性,对抵御来自系统内部的威胁具有明显的优势。     

轻量级的无线射频识别安全认证协议

针对现有无线射频识别(RFID)认证协议存在的安全缺陷,提出了一种新的轻量级RFID安全认证协议,并基于GNY逻辑给出了形式化证明。协议采用阅读器双重认证及预认证阶段刷新密钥的方法,通过在标签中添加保护密钥同步的恶意攻击标记Tm,解决了当前协议中存在的可扩展性欠佳、标签密钥更新失败导致位置跟踪和非法更新标签/服务器内部密钥造成拒绝服务(DoS)等问题,可抵抗重传、标签/阅读器假冒和通信量分析等多种恶意攻击,尤其防范来自位置隐私泄露和拒绝服务的安全威胁。分析结果表明,所提协议具有低成本、安全性高、计算复杂度低等特点,适合于标签数目较多的RFID系统。     

Tracking and Following a Tagged Leopard Shark with an Autonomous Underwater Vehicle

This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real‐time estimation of the shark's two‐dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo‐hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but it does not provide the sign (+ or ?) of the bearing angle. Estimation is accomplished using a particle filter that fuses bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic‐based controller allows the system to overcome the ambiguity in the sign of the bearing angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long‐distance shark‐tracking methods, i.e., manually driven boat‐based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1‐m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. © 2013 Wiley Periodicals, Inc.     

RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments

In recent years, the number of elderly people living alone has grown rapidly. This increases the need for indoor healthcare services that help elderly residents live a safe and independent life. There has been increasing interest in indoor ubiquitous healthcare (U-Healthcare) applications that monitor the elderly unobtrusively via sensors and that warn them or healthcare providers of abnormal conditions. In U-Healthcare applications, automatically locating and tracking users who move around a building is a fundamental feature. Outdoor location sensing technologies such as the global positioning systems are not suitable for use in indoor environments, such as “smart home” applications, because their indoor-based location sensing lacks accuracy. This paper proposes an indoor U-Healthcare system that uses radio-frequency identification technology to accurately locate and track the elderly. The proposed system provides real-time monitoring of elderly people’s whereabouts. In addition, it analyzes their locations in association with time slots and the length of time they stay in the same place, thus inferring information such as movement patterns, ranges, and frequencies. This information is used to determine elderly people’s well-being and to warn family or healthcare workers of any potential problems. The proposed indoor U-Healthcare system improves the quality and convenience of care delivered to elderly people.     

SCaNME: Location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map

In this paper, we propose a novel location tracking system called SCaNME (Shotgun Clustering-aided Navigation in Mobile Environment) which iteratively sequences the clusters of sporadically recorded received signal strength (RSS) measurements and adaptively construct a mobility map of the environment for location tracking. In the SCaNME system, the location tracking problem is solved by first matching the people’s locations to the location points (LPs) with small Kullback–Leibler (KL) divergence. Then, Allen’s logics are applied to reveal the person’s activities, assist the on-line location tracking and finally obtain a refined path estimate. The experimental results conducted on the large-scale HKUST campus demonstrate that the SCaNME tracking system provides better precision and reliability than the conventional location tracking systems. Furthermore, the experiments of SCaNME tracking system show its capability of providing people’s real-time locations without fingerprint calibration in large-scale Wi-Fi environment.     

基于固件的终端位置管理系统研究与应用

现有的终端位置管控方法大多是在计算机外壳粘贴射频识别（RFID）标签进行实时定位。但是,一旦计算机被带离RFID信号覆盖区域,外贴的RFID标签缺乏对计算机的直接管控能力。因此,基于固件技术和RFID技术,提出和设计了基于固件的终端位置管理系统。首先,该系统通过RFID信号划定授权区域,在上电开机阶段,通过固件层实现与RFID标签的交互,仅允许终端在接收到RFID授权信号后才能开机使用;其次,在操作系统运行阶段,计算机需要得到RFID授权信号才能正常使用;再次,通过固件对操作系统中的位置管控软件进行保护,防止管控软件被篡改和删除。在计算机脱离RFID信号覆盖范围,终端中的软件代理将立即捕捉到这种情况,根据安全策略锁定终端或对数据进行销毁。目前已经研制原型系统,对办公区域内30台计算机终端进行位置管理,实现了终端仅在授权区域可正常开机使用,脱离授权区域将立即锁定。     

Improving location awareness in indoor spaces using RFID technology

Location awareness is a key issue to improve the development of autonomous entities that are embedded into ubiquitous computing environments. GPS seems to be the best solution to develop outdoor location systems, but the performance of these systems is not good enough to locate objects or humans within indoor environments, mainly if accuracy and precision are required. In this article we propose the use of a cheap and reliable technology as RFID to develop a passive RFID-based indoor location system that is able to accurately locate autonomous entities, such as robots and people, within a defined surface. This system is applied to solve the robot tracking problem. We include the evaluation of the proposal by comparing our system technology performance with other alternatives built on different technologies (Wi-Fi, Bluetooth, IrDA, ultrasound, etc.). We have also performed a location awareness proof concept test to analyze the viability of the approach.     

An indoor location tracking based on mobile RFID for smart exhibition service

Recently, various smart application services have been developed using GPS (Global Positioning System), RFID (Radio Frequency IDentification) and sensor networks. The GPS has been successfully applied for outdoor location tracking by many applications, but it might still be insufficient in an indoor environment where GPS signals are often severely obstructed. The RFID technology has been utilized to play an important role in location tracking for indoor smart applications. Therefore, in this paper, we present the scenario and architecture of an indoor location tracking service for things or space in an exhibition environment based on mobile RFID. The RFID tags of things or spaces are identified as the locations of point being passed and we obtain the spatial data from the tags using mobile RFID readers, Web server and Database server. We have designed and implemented the prototype of location tracking system for exhibition scenario using Microsoft .NET framework. Additionally, we have verified the functionality of this system so various other indoor smart services may be provided using the proposed system.     

A boundary condition based algorithm for locating construction site objects using RFID and GPS

Construction sites are rugged, dynamic, and complex, involving a large number of objects that continuously change their locations and occupied spaces. Being able to accurately locate and track site objects site is critical to project management and control to meet the increasing demands for efficiency and safety in modern construction projects. Automated locating technologies such as Radio Frequency Identification (RFID) have proven to be beneficial in tracking some construction site objects. The biggest challenge in applying the RFID technology is that the received signal strength (RSS) varies over time and location, and there is no direct relationship between signal strength and detection range, leading to low positional accuracies in the estimated tag locations. This paper presents an algorithm termed BConTri that combines a “boundary condition method” and the trilateration concept to estimate tag location in three-dimensional (3D) real world coordinates from four or more RFID readers equipped with GPS. This study also developed a prototype RFID locating system that implemented the newly created BConTri algorithm. A comprehensive assessment of the positional accuracy was applied to field experiment results and a high accuracy of the algorithm was observed. A measure of the spatial dilution of reader distribution was formulated and a linear relationship between this measure and the locating accuracy was observed, forming the base for using this measure as a reliable indicator for the resulting locating accuracy. This accuracy indicator helps in estimating the accuracy and quality control of RFID-based locating and tracking systems in construction.     

集成RFID的智能建筑系统研究

RFID技术是近年来业界关注的热点,被认为是21世纪最重要的技术之一。本文结合普适计算环境发展的趋势,提出了集成RFID的智能建筑系统原型和模块设计,并对RFID在智能建筑系统中的安全及隐私性设计进行了详细论述。     

个人信息保护的RFID标签信息读取模块设计

RFID技术虽然给我们的日常生活带来了便利,但是此技术的广泛应用同样带来了个人信息安全的盲区。所以在不增加RFID标签成本和用户私人信息管理成本的前提下,提出了一个具有个人信息保护功能的新的系统结构,利用现有的RFID硬件,通过对信息的加密和策略管理,使系统自动、实时地对用户的个人信息进行权限控制和保护,从而加强对用户私有信息的管理和保护,提高用户对商家的信任度和忠诚度。     

Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

High-speed train navigation system based on multi-sensor data fusion and map matching algorithm

Navigation system for high-speed trains is necessary for increased operational safety and efficiency, new services for customers, and low maintenance cost. This paper proposes a high accuracy navigation system for high-speed trains based on a sensor fusion algorithm, with non-holonomic constraints, for multiple sensors, such as accelerometers, gyroscopes, tachometers, Doppler radar, differential GPS, and RFID, and a map matching algorithm. In the proposed system, we consider the federated Kalman filter for sensor fusion, where local filters utilize filter models developed for various sensor types. Especially, the local Kalman filter for RFID positioning, that is detected at irregular time intervals due to the varying train speed and RFID tag spacing, is developed to maintain high performance during GPS outage. In addition, an orthogonal projection map matching algorithm is developed to improve the performance of the proposed system. The performance of the proposed system is demonstrated with numerous simulations for a high-speed train in Korea. The simulation results are analyzed with respect to the existence of tunnel, RFID deployment spacing, RFID location uncertainty, and DGPS error.     

Efficient Services Management in Libraries using AI and Wireless techniques

Radio Frequency IDentification (RFID) has been used in a wide variety of applications such as highway toll collection, building access control, animal tracking, remote keyless entry for automobiles and tracking assets. For example, RFID in libraries, with a modest investment, can improve the capabilities of both librarians and users. This paper presents a new use of RFID in libraries to help determining the physical location of a book within the library and to provide assistance to users to arrive to the desired locations.Our initial prototype, named SIGUEME, has been extended and experimentally implemented in Meco’s Public Library (Madrid, Spain). The application comprises three main systems: an electronic one – which detects users through a RFID sensors system; an AI-based system to plan and to monitor the users’ requests according to their interests; and finally a screen-based information system that communicates visually the directions the users have to follow. We have developed a wireless communications system based on Zigbee technology to allow both, the information flow across the building and to connect the antennas to detect RFID users. This eliminates the need for using wires.The application not only helps users to easily locate the books or the reading rooms inside the building, but also automates tasks manually performed by the library staff (i.e. generate statistics about books usage time, age-related usage, etc).     

基于Beowulf机群中改进粒子滤波的3D人体运动跟踪

针对标准的粒子滤波算法在视频三维人体运动跟踪中存在的计算量巨大、粒子退化、跟踪失效而无法同时满足跟踪精度和跟踪实时性要求的问题,提出了基于Beowulf机群中改进的粒子滤波新算法。新算法通过三维人体模型参数的自动初始化、粒子数目和模板的调整来实现跟踪失效的自动恢复,基于任务动态分配策略、低开销通信策略设计的Beowulf机群中的迁移式粒子滤波并行算法克服了粒子退化问题和提高了计算速度。实验结果显示：新方法有效地减轻了粒子退化和跟踪失效问题,降低了计算时间,提高了跟踪精度,能够同时满足三维人体运动跟踪精度和实时性的要求。     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

A proximity-based method for locating RFID tagged objects

This paper presents a method intended to extend the use of current radio frequency identification (RFID) technology to tracking the precise location of tagged materials on construction sites. The performance experienced with a commercially available RFID system is compared with the theoretical performance derived from an analytical discrete framework. Also through experimentation, the effects of parameters including RF power, the number of reads, and tag density are assessed, and their performance trade-offs are characterized to suggest guidelines for potential field deployment.     

基于有限状态机的RFID流数据过滤与清理技术

为了实现从大量不可靠、冗余的RFID（radio frequency identification）流数据中提取有效信息,提高RFID系统中数据的质量,提出了一种基于有限状态机的RFID 流数据过滤与清理方法.实验结果表明：该方法能够有效过滤系统外标签数据,清理系统内部冗余标签数据,筛选有效标签数据,并能够降低漏读、误读带来的风险.最后,利用地理信息系统的可视化技术,将过滤与清理结果展示在地图上.