Indoor Pedestrian Tracking Research Articles

Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes

In order to solve the indoor pedestrian positioning and tracking problems under the condition of sparse anchor nodes, this paper presents a new tracking scheme which predicts the staff position under the condition of indoor location fingerprints based on particle filter. In the proposed algorithm, the indoor topology is adopted to constrain and correct the results. Simulation results show that the proposed algorithm can significantly improve the accuracy of indoor pedestrian positioning and tracking more than the Kalman filter and k-nearest neighbor (KNN) algorithms. The simulation results also show that under the condition of sparse nodes deployment good tracking results can still be achieved through the adoption of indoor topology and the average positioning error is about 1.9 m.

A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.

Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the Received Signal Strength (RSS) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyzed various aspects of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is tolerant to the RSS variance problem.

Smartphone-based indoor pedestrian tracking using geo-magnetic observations

With the widespread use of smartphones, the use of location-based services LBS with smartphones has become an active research issue. The accurate measurement of user location is necessary to provide LBS. While outdoor locations are easily obtained with GPS, indoor location information is difficult to acquire. Previous work on indoor location tracking systems often relied on infrastructures that are influenced by environmental changes and temporal differences. Several studies have proposed infrastructure-less systems that are independent of the surroundings, but these works generally required non-trivial computation time or energy costs. In this paper, we propose an infrastructure-less pedestrian tracking system in indoor environments. The system uses accelerometers and magnetic sensors in smartphones without pre-installed infrastructure. We reduced the cumulative error of location tracking by geo-magnetic observations at corners and spots with magnetic fluctuations. In addition, we developed a robust estimation model that is tolerant to false positives, as well as a mobility model that reflects the characteristics of multiple sensors. Extensive evaluation in a real environment indicates that our system is accurate and cost-effective.

Unsupervised Construction of an Indoor Floor Plan Using a Smartphone

Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.

Economic and Synergistic Pedestrian Tracking System with Service Cooperation for Indoor Environments

This paper describes an indoor pedestrian tracking system that can economically improve the tracking performance and the quality and value of services by incorporating other services synergistically. The tracking system obtains position, orientation, and action of pedestrians continuously and accurately in large indoor environments by utilizing surveillance cameras and active RFID tags for security services and 3-D environment models for navigation services. Considering service cooperation and co-creative intelligence cycles, this system can improve both the tracking performance and the quality of services without significant increase of costs by sharing the existing infrastructures and the 3-D models among services. The authors conducted an evaluation of the tracking system in a large indoor environment and confirmed that the accuracy of the system can be improved by utilizing the infrastructures and the 3-D models. Synergistic services utilizing the tracking system and service cooperation can also enhance the quality and value of services.