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

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.