Mobile tracking in mixed line-of-sight/non-line-of-sight conditions: Algorithms and theoretical lower bound.
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific
|Title of host publication||Handbook of Position Location: Theory, Practice, and Advances, 2nd Edition|
|Editors||Reza Zekavat, R. Michael Buehrer|
|Number of pages||24|
|Publication status||Published - 1 Feb 2019|
|Publication type||B2 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.