Remote Sensing Technologies for Indoor Applications
Seong-hoon Peter Won, William Melek, Farid Golnaraghi · 2011
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