Han Zou

Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

A Robust Indoor Positioning System Based on the Procrustes Analysis and Weighted Extreme Learning Machine

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity.

An online sequential extreme learning machine approach to WiFi based indoor positioning

Developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location Based Service (LBS) in indoor environment recently. WiFi technology has been studied and explored to provide indoor positioning service for years since existing WiFi infrastructures in indoor environment can be used to greatly reduce the deployment costs. A large body of WiFi based IPSs adopt the fingerprinting approach as the localization algorithm. However, these WiFi based IPSs suffer from two major problems: the intensive costs on manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on online sequential extreme learning machine (OS-ELM) to address these problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey, and more importantly, its online sequential learning ability enables the proposed localization algorithm to automatically and timely adapt to the environmental dynamics. The experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches due to its fast adaptation to various environmental changes.

An RFID indoor positioning system by using weighted path loss and extreme learning machine

Radio Frequency Identification (RFID) technology has been widely used in many application domains. How to apply RFID technology to develop an Indoor Positioning System (IPS) has become a hot research topic in recent years. LANDMARC approach is one of the first IPSs by using active RFID tags and readers to provide location based service in indoor environment. However, major drawbacks of the LANDMARC approach are that its localization accuracy largely depends on the density of reference tags and the high cost of RFID readers. In order to overcome these drawbacks, two localization algorithms, namely weighted path loss (WPL) and extreme learning machine (ELM), are proposed in this paper. These two approaches are tested on a novel cost-efficient active RFID IPS. Based on our experimental results, both WPL and ELM can provide higher localization accuracy and robustness than existing ones.

BlueDetect: An iBeacon-Enabled Scheme for Accurate and Energy-Efficient Indoor-Outdoor Detection and Seamless Location-Based Service

The location and contextual status (indoor or outdoor) is fundamental and critical information for upper-layer applications, such as activity recognition and location-based services (LBS) for individuals. In addition, optimizations of building management systems (BMS), such as the pre-cooling or heating process of the air-conditioning system according to the human traffic entering or exiting a building, can utilize the information, as well. The emerging mobile devices, which are equipped with various sensors, become a feasible and flexible platform to perform indoor-outdoor (IO) detection. However, power-hungry sensors, such as GPS and WiFi, should be used with caution due to the constrained battery storage on mobile device. We propose BlueDetect: an accurate, fast response and energy-efficient scheme for IO detection and seamless LBS running on the mobile device based on the emerging low-power iBeacon technology. By leveraging the on-broad Bluetooth module and our proposed algorithms, BlueDetect provides a precise IO detection service that can turn on/off on-board power-hungry sensors smartly and automatically, optimize their performances and reduce the power consumption of mobile devices simultaneously. Moreover, seamless positioning and navigation services can be realized by it, especially in a semi-outdoor environment, which cannot be achieved by GPS or an indoor positioning system (IPS) easily. We prototype BlueDetect on Android mobile devices and evaluate its performance comprehensively. The experimental results have validated the superiority of BlueDetect in terms of IO detection accuracy, localization accuracy and energy consumption.

Robust Extreme Learning Machine With its Application to Indoor Positioning

The increasing demands of location-based services have spurred the rapid development of indoor positioning system and indoor localization system interchangeably (IPSs). However, the performance of IPSs suffers from noisy measurements. In this paper, two kinds of robust extreme learning machines (RELMs), corresponding to the close-to-mean constraint, and the small-residual constraint, have been proposed to address the issue of noisy measurements in IPSs. Based on whether the feature mapping in extreme learning machine is explicit, we respectively provide random-hidden-nodes and kernelized formulations of RELMs by second order cone programming. Furthermore, the computation of the covariance in feature space is discussed. Simulations and real-world indoor localization experiments are extensively carried out and the results demonstrate that the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviation and worst case error of IPSs compared with other baseline algorithms.

Platform and Algorithm Development for a RFID-Based Indoor Positioning System

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

Environmental sensing by wearable device for indoor activity and location estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant-carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.

An integrative Weighted Path Loss and Extreme Learning Machine approach to Rfid based Indoor Positioning

In recent years, applying RFID technology to develop an Indoor Positioning System (IPS) has become a hot research topic. The most prominent advantage of active RFID IPS comes from its unique identification of different objects in indoor environment. However, certain drawbacks of existing RFID IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the location based service, largely limit the applications of active RFID IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID IPS by using cheaper active RFID tags, sensors and reader. In addition, one localization algorithm: integrated Weighted Path Loss (WPL) - Extreme Learning Machine (ELM) which combines the fast estimation of WPL and the high localization accuracy of ELM is proposed. According to the algorithm, an indoor environment is divided into small zones firstly and an ELM model is developed for each zone during the offline phase. During the online phase, the WPL approach is used to determine the zone of the target primarily, then the ELM model of that zone is deployed to provide the final estimated location of the target. Based on our experimental result, this integrated algorithm provides a higher localization efficiency and accuracy than existing approaches.