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Mobile tracking in mixed line-of-sight/non-line-of-sight conditions: Algorithms and theoretical lower bound.

Research output: Chapter in Book/Report/Conference proceedingChapterScientific


Original languageEnglish
Title of host publicationHandbook of Position Location: Theory, Practice, and Advances, 2nd Edition
EditorsReza Zekavat, R. Michael Buehrer
Number of pages24
ISBN (Print)978-1-119-43458-0
Publication statusPublished - 1 Feb 2019
Publication typeB2 Part of a book or another research book


This chapter investigates the problem of mobile racking in mixed line-of-sight (LOS)/non-line-of sight (NLOS) conditions. The state-of-the-art methods in this field are first reviewed. Then, we consider the problem in the Bayesian estimation framework and focus on two types of Bayesian filters: the Gaussian mixture filter (GMF) and the particle filter (PF). In the GMF section, the approximation property an d the convergence results are summarized. Then, the modified extended Kalman filter (EKF) banks method, as one specific GMF, is described. In the PF section, generic PF is first introduced, and a more effective PF, approximated Rao-Blackwellized particle filtering (ARBPF), is further discussed of a posterior Cramer-Rao lower bound (CRLB) for this kind of mobile tracking problem.

Field of science, Statistics Finland