Indoor pedestrian navigation based on recursive filtering (Seminar Paper)

I decided to share my seminar with you which I did back in the summer semester 2013. It is a survey paper about indoor pedestrian navigation. The paper is a good starting point to introduce you to the topic and provides you with interesting literature.

While localization is most commonly associated with GPS, many use cases remain where satellite-based navigation is too inaccurate or fails completely. In this seminar, we will present techniques usable for indoor localization of pedestrians. We will introduce several approaches using Inertial Measurement Units attached to the subject. Due to the strong drifting behavior of those units, several steps are necessary to provide feasible accuracy: the use of filter techniques and the use of Zero Velocity Updates. We will explain the required state-space
model and its application in recursive Bayesian filters like the Extended Kalman Filter or the Particle Filter. The use of aiding techniques is discussed and a map-aided, WiFi-initialized Particle Filter is presented.

Kalman Filter

Figure from the seminar paper showing the steps of the Extended Kalman Filter (EKF).

@MISC{Hasper2013,
author = {Hasper, Philipp and Bleser, Gabriele},
title = {Indoor pedestrian navigation based on recursive filtering},
howpublished = {Seminar Paper},
year = {2013},
keywords = {Sensor fusion, Recursive Filtering, Kalman Filter, Particle Filter,
ZUPT, EKF, MCL}
}

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