WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors

Indoor tracking systems have become very popular, wherein pedestrian movement is analyzed in a variety of commercial and secure spaces. The inertial sensor-based method makes great contributions to continuous and seamless indoor pedestrian tracking. However, such a system is vulnerable to the cumulative locating errors when moving distance increases. Inaccurate heading values caused by the interference of body swing of natural walking and the geomagnetic disturbances are the main sources of the accumulative errors. To reduce such errors, additional infrastructure or highly accurate sensors have been used by previous works that considerably raise the complexity of the architecture. This paper presents an indoor pedestrian tracking system called WTrack, using only geomagnetic sensors and acceleration sensors that are commonly carried by smartphones. A fine-grained walk pattern of indoor pedestrians is modeled through Hidden Markov Model. With this model, WTrack can track indoor pedestrians by continuously recognizing the pre-defined pedestrians’ walk pattern. More importantly, WTrack is able to resist both the interference of body swing of natural walking and the geomagnetic disturbances of nearby objects. Our experimental results reveal that the location error is <2 m, which is considered adequate for indoor location-based-service applications. The adaptive sample rate adjustment mode further reduces the energy consumption by 52 % in comparison, as opposed to the constant sampling mode.     

Remote service discovery and binding architecture for soft real-time QoS in indoor location-based service

Indoor location-based service (LBS) is generally distinguished from web services that have no physical location and user context. In particular, various resources have dynamic and frequent mobility in indoor environments. In addition, an indoor LBS includes numerous service lookups being requested concurrently and frequently from several locations, even through a network infrastructure requiring high scalability in indoor environments. The traditional centralized LBS approach needs to maintain a geographical map of the entire building or complex in its central server, which can cause low scalability and traffic congestion. This paper presents a self-organizing and fully distributed indoor LBS platform with regional cooperation among devices. A service lookup algorithm based on the proposed distributed architecture searches for the shortest physical path to the nearest service resource. A continuous service binding mechanism guarantees a probabilistic real-time QoS regardless of dynamic and frequent mobility in a soft real-time system such as an indoor LBS. Performance evaluation of the proposed algorithm and platform is compared to the traditional centralized architecture in the experimental evaluation of scalability and real test bed environments.     

Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors

Researchers have recently devoted considerable attention to acquiring location awareness of assets. They have explored various technologies, such as video cameras, radio signal strength indicator-based sensors, and motion sensors, in the development of tracking systems. However, each system presents unique drawbacks especially when applied in complex indoor construction environments; this paper classifies them into two categories: absolute tracking and relative tracking. By understanding the nature of problems in each tracking category, this research develops a novel tracking methodology that uses knowledge of the strengths and weaknesses of various components used in the proposed tracking system. This paper presents the development of a hybrid-tracking system that integrates Bluetooth Low Energy (BLE) technology, motion sensors, and Building Information Model (BIM). The hypothesis tested through this integration was whether such knowledge-based integration could provide a method that can correct errors found in each of the used sensing technologies and thereby improve the reliability of the tracking system. Field experimental trials were conducted in a full-scale indoor construction site to assess the performance of individual components and the integrated system. The results indicated that the addition of map knowledge from a BIM model showed the capability of correcting improbable movements. Furthermore, the knowledge-based decision making process demonstrated its capability to make positive interaction by reducing the positioning errors by 42% on average. In sum, the proposed hybrid-tracking system presented a novel method to compensate for the weakness of each system component and thus achieve a more accurate and precise tracking in dynamic and complex indoor construction sites.     

Map assisted PDR/Wi-Fi fusion for indoor positioning using smartphone

In this paper we present a map-assisted pedestrian navigation system for smartphone user which combines map information, IMU-based Pedestrian Dead Reckoning (PDR) and Wi-Fi localization using fingerprinting method. PDR (Pedestrian Dead Reckoning) using smartphone consist with step detection, step length estimation and heading estimation. However, these algorithms have errors caused by various reasons such as step length error at uncertain user, magnetic disturbance in indoor situation and unstable position of smartphone. To increase accuracy of the PDR, Wi-Fi fusion or map matching method has been proposed. However, previous methods could not solve fault matching or creating map in hall area. Especially in hall, pedestrian could make various trajectories that accurate map structures are required. For solving the structure of map database in hall problem and accurate link selection, we propose a Virtual Link (VL) algorithm with a Virtual Track (VT). Furthermore, an Extended Kalman Filter (EKF) is used for estimating pedestrian position and IMU sensor errors. With map information, step length estimation error, heading error at pedestrian dead reckoning and some IMU sensor errors are estimated. Real world experiments are conducted at building, and it shows less than 3m of CEP (Circular Error Probability) after 200m walk.     

GETA sandals: a footstep location tracking system

This paper presents the design, implementation, and evaluation of a footstep based indoor location system. The traditional Japanese GETA sandals are equipped with force, ultrasonic, orientation, RFID sensors and an accelerometer to produce a wearable location tracking system that demand little infrastructure in the deployed environment. In its basic form, a user simply puts on GETA sandals to enable tracking of his/her locations relative to a starting point (e.g., a building entrance), making it easy for deployment everywhere. The footstep location system is based on dead-reckoning, which works by measuring and tracking displacement vectors along a trail of footsteps. Each displacement vector is formed by drawing a line between each pair of footsteps, and the position of a user can be calculated by summing up the current and all previous displacement vectors. Unlike most existing indoor location systems, the footstep based method does not suffer from problems with obstacles, multi-path effects, signal noises, signal interferences, and dead spots. There are two technical challenges in the proposed design: (1) location error accumulates over distance traveled, and (2) displacement measurements are sporadic during stair climbing. The first problem is addressed by a light RFID infrastructure, while the second problem is remedied by incorporating an accelerometer into the system. Experiments on GETA prototype are conducted to evaluate the positional accuracy of our system.     

Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user’s exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce communication resources, and network partitions, we propose three key features to enhance our algorithm: (1) An information sharing scheme enables mobile users to share their gathered peer location information to reduce communication overhead; (2) A historical location scheme allows mobile users to utilize stale peer location information to overcome the network partition problem; and (3) A cloaked area adjustment scheme guarantees that our spatial cloaking algorithm is free from a “center-of-cloaked-area” privacy attack. Experimental results show that our P2P spatial cloaking algorithm is scalable while guaranteeing the user’s location privacy protection.     

Improvement of Kalman filters for WLAN based indoor tracking

Location Based Service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, Extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (Wireless Local Area Network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. This paper proposes a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. After analyzing the experimental results of it, we modified it to use K-NN method in the measurement stage of it. Then we propose to further improve the accuracy of indoor tracking by adjusting the parameter values referring to the map information. Experimental results comparing our method with other previous methods are discussed.     

A 3D indoor positioning system based on low-cost MEMS sensors

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.     

基于神经网络的多传感器融合PDR定位方法

针对当前行人航位推算系统因行人随意性行走、传感器漂移等造成行人步长估计不精确、方向计算误差累积问题,提出了一种基于神经网络和智能手机内置多传感器融合的PDR室内定位方法.首先利用加速计采集的传感器数据和移动距离数据训练BP神经网络,将训练好的BP神经网络模型进行行人移动距离预测,然后根据行人行走步伐的连续性特点和传感器输出之间的相关性,设计了一种微航向角融合的方向估计算法.该算法通过对行走过程中的情况进行分类以获得可靠的传感器源,利用3种微航向角进行分类加权融合,最终获得行人行走方向的精确估计.实验结果表明,通过行人移动距离预测和微航向角融合算法能够实现得较好的定位效果.     

Indoor location based services challenges,requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge.     

A smartphone-based multimodal indoor tracking system

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly integrated in most smartphones have already shown promising and better results than location solutions based on a single data source. In this paper, we present a multi-sensor tracking system built by incrementally integrating state-of-the-art models of the Wi-Fi interface and the accelerometer, gyroscope and magnetometer sensors of a smartphone. Our proposal consists of a simple calibration phase of the tracking system, which involves enabling simultaneous data gathering from all three sensors and the Wi-Fi interface. Taking the Wi-Fi signal model as baseline, four different configurations are evaluated by incrementally adding and integrating the models of the other three sensors. The experimental results reveal a mean error accuracy of 60 cm in the case when the tracking system makes use of all four data sources. Our results also include a spatial characterization of the accuracy and processing power requirements of the proposed solution. Our main findings demonstrate the feasibility of developing accurate localization indoor tracking systems using current smartphones without the need for additional hardware.     

Recommendation system using location-based ontology on wireless internet: An example of collective intelligence by using ‘mashup’ applications

Location-Based Service (LBS) is considered as a key component of upcoming ubiquitous environments. A recommendation system based on LBS is expected to be an important service in ubiquitous environments, and most hardware technologies such as location estimation of a user by using Global Positioning System (GPS), as well as hi-speed internet access through cell phones, are currently supported. However, in terms of software, most services are driven and supported by a LBS service provider only. Consequently, lack of participation of users may occur in mobile environments. In this study, we suggest a LBS knowledge base inference platform with ontology which considers the current location and available time of users. Our knowledge base supports user participation as collective intelligence. We mashed up Open Application Programming Interface (OpenAPI) for scalable implementation of the system. Through experiments, we show that a user can build up his/her knowledge base, and by using this information, the system recommends to other users appropriate information that matches the user’s condition and profile through inference.     

Evaluation of realtime people tracking for indoor environments using ubiquitous motion sensors and limited wireless network infrastructure

We present the development and evaluation of a realtime indoor localisation system for tracking people. Our aim was to track a person’s indoor position using dead-reckoning, while limiting position error without depending on extensive wireless network infrastructure. The Indoor People Tracker used wearable motion sensors, a floor-plan map and a limited wireless sensor network for proximity ranging. We evaluated how the position accuracy of the Indoor People Tracker was affected by floor-plan map features, wireless proximity range and motion information. The advantage of the Indoor People Tracker was found; it was able to achieve accurate position resolution with minimal error, while not depending on wireless proximity.     

Providing location based information/advertising for existing mobile phone users

In the emerging world of m-commerce potential users consistently cite location based information as one of the emergent services that they would most likely utilise. However, solutions for obtaining the specific location of a mobile user predominately rely on the provision of additional hardware and/or software within either the mobile phone or system infrastructure. Further, these techniques are often inappropriate for indoor and highly urban environments, where they are often most useful, as the line of sight to the location measurement unit is often obscured resulting in inaccurate and unreliable positional information. In this paper we present a system that can be used with any current mobile phone system to provide location based information/advertisements to any mobile phone, equipped with Bluetooth technology, without any necessity of installing client side software. The system is readily deployable and can be used to provide systems such as location based information for tourist in cities or museums or indeed location based advertisements.     

基于多传感器数据融合的室内行人航位推算法

针对惯性测量单元(IMU)本身存在测量漂移,很难获得精确的室内行人轨迹的问题,提出了使用多个传感器信息融合的方案,包括IMU和视频摄像头,并结合卡尔曼滤波和零速检测算法进行参数优化,以提高行人运动轨迹的精度.仿真结果表明:算法可以有效降低行人轨迹的误差.     

User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding

Though research into location-based services (LBS) is being carried out across a number of disciplines, user aspects of LBS remains a cross-cutting theme. In this paper, the research focuses on investigating the user information requirements from LBS at individual level, with emphasis on the interactive nature of information transactions between environments, individuals and mobile devices. Based on a proposed conceptual model, urban pedestrian wayfinding experiments have been implemented in an immersive virtual reality test environment. Automated and semi-automated methods of data collection have allowed an integrated picture of participant behaviour and information preferences to be constructed and analysed. The results of this study show that there are clear user preferences in information requirements in completing wayfinding tasks. However, changes in user preferences during the wayfinding tasks do occur in response to levels of confidence, different spatial layouts and the wayfinding situations individuals encounter. The outcomes indicate that the proposed conceptual interaction model and adopted implementation approach assist in understanding user behaviour and information preferences for LBS.     

结合位置指纹和行人航迹的室内定位技术

主要对室内定位技术展开研究,首先通过手持智能设备收集指定范围样本点的坐标及wifi热点信息;然后应用位置指纹定位方法进行绝对定位;为了提高行走过程中定位的准确性和实时性,采用行人航迹推算算法,即通过手机传感器采集并经处理的数据进行步频检测、步长估算和方向检测,实现相对位置变化的估算.行人航迹算法克服位置指纹定位的不稳定性,而位置指纹定位算法及时调整行人航迹算法带来的累积误差.实验结果表明两种室内定位技术的结合有效提高了室内定位的准确性,能充分应用到实际生活中.     

Online Approach for Spatio-Temporal Trajectory Data Reduction for Portable Devices

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but effcient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.     

基于GLRT零速检测算法的行人室内定位系统

针对微机电系统中惯性传感器漂移大、精度低导致室内行人定位精度不高的问题,本系统在惯性导航解算算法的基础上,提出基于广义似然比检验的零速检测算法.该方法是利用广义似然比检验对行人处于站立相或摆动相的概率进行估计以及进行零速更新,提高行人定位精度.基于本文提出的行人室内定位模型,搭建以惯性测量单元为核心的实验平台,评估本文算法的可行性.实验结果表明行人定位的动态误差为-1.8141 m~1.4516 m,置信度为97.61%.表明本文的行人室内定位系统满足实际定位的要求.     

IMAF: in situ indoor modeling and annotation framework on mobile phones

The widespread use of smart phones with GPS and orientation sensors opens up new possibilities for location-based annotations in outdoor environments. However, a completely different approach is required for indoors. In this study, we introduce IMAF, a novel indoor modeling and annotation framework on a mobile phone. The framework produces a 3D room model in situ with five selections from user without prior knowledge on actual geometry distance or additional apparatus. Using the framework, non-experts can easily capture room dimensions and annotate locations and objects within the room for linking virtual information to the real space represented by an approximated box. For registering 3D room model to the real space, an hybrid method of visual tracking and device sensors obtains accurate orientation tracking result and still achieves interactive frame-rates for real-time applications on a mobile phone. Once the created room model is registered to the real space, user-generated annotations can be attached and viewed in AR and VR modes. Finally, the framework supports object-based space to space registration for viewing and creating annotations from different views other than the view that generated the annotations. The performance of the proposed framework is demonstrated with achieved model accuracy, modeling time, stability of visual tracking and satisfaction of annotation. In the last section, we present two exemplar applications built on IMAF.