20. Remote Sensing Technologies for Indoor Applications

  1. Seyed A. (Reza) Zekavat3 and
  2. R. Michael Buehrer4
  1. Seong-Hoon Peter Won1,
  2. William Wael Melek1 and
  3. Farid Golnaraghi2

Published Online: 6 SEP 2011

DOI: 10.1002/9781118104750.ch20

Handbook of Position Location: Theory, Practice, and Advances

Handbook of Position Location: Theory, Practice, and Advances

How to Cite

Won, S.-H. P., Melek, W. W. and Golnaraghi, F. (2011) Remote Sensing Technologies for Indoor Applications, in Handbook of Position Location: Theory, Practice, and Advances (eds S. A. (. Zekavat and R. M. Buehrer), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118104750.ch20

Editor Information

  1. 3

    Michigan Technological University, Houghton, MI, USA

  2. 4

    Virginia Tech, Blacksburg, VA, USA

Author Information

  1. 1

    University of Waterloo, Waterloo, Canada

  2. 2

    Simon Fraser University, Surrey, Canada

Publication History

  1. Published Online: 6 SEP 2011
  2. Published Print: 16 SEP 2011

ISBN Information

Print ISBN: 9780470943427

Online ISBN: 9781118104750

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Keywords:

  • Bayesian filter;
  • extended Kalman filter (EKF);
  • indoor position tracking systems;
  • Kalman filter (KF);
  • particle filter (PF);
  • remote sensing technologies

Summary

The need for remote sensing systems in various areas such as medical, manufacturing, military, and automation fields is constantly growing. This demand led to the development of position sensors that can be used for indoor applications. Existing indoor sensors can be categorized as (1) vision-based, (2) non-vision-based, and (3) inertial. This chapter reviews these three categories of remote sensing technologies and discusses their advantages and limitations as well as their applications. Many recent remote sensing systems use state estimators to achieve higher accuracy or to hybridize sensors. As a state estimator, a variant of Bayesian filter such as the Kalman filter (KF) or the particle filter (PF) is widely used. The chapter presents the fundamental concepts of Bayesian filter, and PF, and reviews KF, extended Kalman filter (EKF).

Controlled Vocabulary Terms

indoor communication; Kalman filters; particle filtering (numerical methods); position measurement; remote sensing