Pedestrian Inertial Navigation with Self-Contained Aiding. Andrei M. Shkel

Pedestrian Inertial Navigation with Self-Contained Aiding - Andrei M. Shkel


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      Library of Congress Cataloging‐in‐Publication Data applied for:

      ISBN: 9781119699552

      Cover Design: Wiley

      Cover Image: © Production Perig/Shutterstock

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      Yusheng Wang, PhD, received the B.Eng. degree (Hons.) in engineering mechanics from Tsinghua University, Beijing, China, in 2014 and the Ph.D. degree in mechanical and aerospace engineering from the University of California, Irvine, CA, in 2020. His research interests include the development of silicon‐based and fused quartz‐based MEMS resonators and gyroscopes, and pedestrian inertial navigation development with sensor fusion. He is currently working at SiTime Corporation as an MEMS Development Engineer.

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      Andrei M. Shkel, PhD, has been on faculty at the University of California, Irvine since 2000, and served as a Program Manager in the Microsystems Technology Office of DARPA. His research interests are reflected in over 300 publications, 42 patents, and 3 books. Dr. Shkel has been on a number of editorial boards, including Editor of IEEE/ASME JMEMS, Journal of Gyroscopy and Navigation, and the founding chair of the IEEE Inertial Sensors. He was awarded the Office of the Secretary of Defense Medal for Exceptional Public Service in 2013, and the 2009 IEEE Sensors Council Technical Achievement Award. He is the President of the IEEE Sensors Council and the IEEE Fellow.

      1.1 Navigation

      The idea of position fixing is straightforward, but the disadvantage is also obvious. Observation of landmarks may not always be available and is susceptible to interference and jamming. For example, no celestial measurement is available in foggy or cloudy weather; radio signals suffer from diffraction, refraction, and Non‐Line‐Of‐Sight (NLOS) transmission; satellite signals may also be jammed or spoofed. Besides, a known “map” is required, which makes this type of navigation infeasible in the completely unknown environment.

      An alternative navigation type is called dead reckoning. The phrase “dead reckoning” probably dated from the seventeenth century, when the sailors calculated their location on the sea based on the velocity and its orientation. Nowadays, dead reckoning refers to the process where the current state (position, velocity, and orientation) of the system is calculated based on the knowledge of its initial state and measurement of speed and heading [4]. Velocity is decomposed into three orthogonal directions based on heading and then multiplied by the elapsed time to obtain the position change. Then, the current position is calculated by summing up the position change and the initial position. A major advantage of dead reckoning over position fixing is that it does not require the observations of the landmarks. Thus, the system is less susceptible to environmental interruptions. On the other hand, dead reckoning is subject to cumulative errors. For example, in automotive navigation, the odometer calculates the traveled distance by counting the number of rotations of a wheel. However, slipping of the wheel or a flat tire will result in a difference between the assumed and actual travel distance, and the error will accumulate but cannot be measured or compensated, if no additional information is provided. As a result, navigation error will be accumulated as navigation time increases.

      Inertial navigation is a widely used dead reckoning method, where inertial sensors (accelerometers and gyroscopes) are implemented to achieve navigation purpose in the inertial frame. The major advantage of inertial navigation is that it is based on the Newton's laws of motion and imposes no extra assumptions on the system. As a result, inertial navigation is impervious to interference and jamming, and its application is universal in almost all navigation scenarios [5].


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