WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Hui, Wang; Lenz, H.; Szabo, Andrei; Bamberger, Joachim; Hanebeck, Uwe D.

doi:10.1109/wpnc.2007.353604

Cited by 147 publications

(100 citation statements)

References 16 publications

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“…Using various filtering techniques can enhance the accuracy of various technologies. Although UWB based technologies have the highest accuracy as of now [53], beacon based microlocation services' accuracy can be enhanced by using filters such as Kalman Filter [87], Extended Kalman Filter [87] and Particle filters [88], [44] etc. There is need for further research to identify the optimal filter for micro-location and how it can further be improved to give us the best possible accuracy.…”

Section: Accuracymentioning

confidence: 99%

Microlocation for Internet-of-Things-Equipped Smart Buildings

Zafari

Papapanagiotou

Christidis

2016

IEEE Internet Things J.

182

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Abstract-Micro-location is the process of locating any entity with high accuracy (possibly in centimeters), while geofencing is the process of creating a virtual fence around a so-called Point of Interest (PoI). In this paper, we present an insight into various micro-location enabling technologies and services. We also discuss how these can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that microlocation based location-aware solutions can play a significant role in facilitating the tenants of an IoT equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using micro-location enabling technologies. We describe the challenges and propose some potential solutions such that micro-location enabling technologies and services are thoroughly integrated with IoT equipped smart building.

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Section: Accuracymentioning

confidence: 99%

Microlocation for Internet-of-Things-Equipped Smart Buildings

Zafari

Papapanagiotou

Christidis

2016

IEEE Internet Things J.

182

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“…Mobile phones are by far the most ubiquitous body-worn devices in the world today and already possess the required sensors for on-body movement sensing and WiFi localization. Common techniques using accelerometers involve measuring the number of zero-crossings of vertical acceleration signals [11] or signals above a certain threshold [12] to obtain step counts and correlating these to speed. These techniques are prone to errors imposed by improper mounting of the phone, incorrect calibration caused by differing walking styles or variance in signal magnitude.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Vathsangam

Tulsyan

Sukhatme

2011

2011 International Conference on Body Sensor Networks

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Abstract-Indoor localization is a promising area with applications in in-home monitoring and tracking. Fingerprinting and propagation model-based WiFi localization techniques have limited spatial resolution because of grid or graph-based representations. An alternative is to incorporate dynamics models based on real-time sensing of human movement and fuse these with WiFi measurements. We present a data-driven dynamic model that tracks the inherent periodicity in walking and converts this representation into velocity. This model however is prone to drift. We correct this drift with WiFi measurements to obtain a combined position estimate. Our approach records and fuses human body movement with WiFi positioning using a single mobile phone. We characterize the movement model to obtain an estimate of error predictions. The movement model showed a best case average RMS prediction error of .25 m/s. We also present a preliminary study characterizing combined system performance across straight line and L-shaped trajectories. The framework showed lower errors across the L-shaped trajectories (mean error ≈ 4.8 m using movement sensing versus a mean error ≈ 6 m without movement sensing) because of the ability assess the validity of a WiFi measurement. Higher errors were observed across the straight line trajectory due to imprecise trajectories.

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“…Another solution is to integrate other location related information besides the WLAN signal. For instance, in our previous work [4] and [5], MEMS sensors were used to enhance the localization performance due to their small sizes and low prices.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems

Wang¹,

Szabo²,

Bamberger³

et al. 2009

Lecture Notes in Electrical Engineering

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Abstract-Typical WLAN based indoor positioning systems take the received signal strength (RSS) as the major information source. Due to the complicated indoor environment, the RSS measurements are hard to model and too noisy to achieve a satisfactory 3-D accuracy in multi-floor scenarios. To enhance the performance of WLAN positioning systems, extra information sources could be integrated. In this paper, a Bayesian framework is applied to fuse multi-information sources and estimate the spatial and time varying parameters simultaneously and adaptively. An application of this framework, which fuses pressure measurements, a topological building map with RSS measurements, and simultaneously estimates the pressure sensor bias, is investigated. Our experiments indicate that the localization performance is more accurate and robust by using our approach.

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WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Cited by 147 publications

References 16 publications

Microlocation for Internet-of-Things-Equipped Smart Buildings

Microlocation for Internet-of-Things-Equipped Smart Buildings

A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems

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