Mobile Tracking in Mixed Line‐of‐Sight/Non‐Line‐of‐Sight Conditions: Algorithms and Theoretical Lower Bound

This chapter investigates the problem of mobile tracking in mixed line‐of‐sight (LOS)/non‐line‐of‐sight (NLOS) conditions. It reviews the state‐of‐the‐art methods in this field. The chapter considers 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 and 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 discussed in detail. The chapter closes with a discussion on the computation of a posterior Cramer‐Rao lower bound (CRLB) for this kind of mobile tracking problem. Simulation results are provided to compare the performance of the filtering algorithms and the posterior CRLB.