Browsing by Author "O'Keefe, Kyle P.G."

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    GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
    (Hindawi Publishing Corporation, 2013-01) Navarro-Alvarez, Ernesto; Siller, Mario; O'Keefe, Kyle P.G.
    We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Radio Frequency (RF) transceivers and position data with a consumer-grade GPS receiver. The novelty of this work lies in the formulation of signal propagation conditions as a parametric observation model in order to estimate first the PLE and then the distance from the received RF signals using nonlinear least squares. GPS data is used to identify long term fading from the received signal's power and helps to refine the power-distance model. Ray tracing geometries for urban canyon (direct line of sight) and nonurban canyon (obstacles) propagation scenarios are used as the physics of the model (design matrix). Although the method was implemented for a lightweight localization algorithm for the 802.11b/g (Wi-Fi) standard, it can also be applied to other ISM band protocols such as 802.15.4 (Zigbee) and 802.15.1 (Bluetooth).
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    Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
    (Multidisciplinary Digital Publishing Institute, 2018-04-17) Gakne, Paul Verlaine; O'Keefe, Kyle P.G.
    This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.