ABSTRACT
This chapter discusses deterministic sample point filters in the presence of random delay in measurement. It deals with nonlinear estimation methods for any arbitrary step randomly delayed measurements. All the filters assume that the measurement is available at every time instant without any delay. The delay that arises during transmission is generally not deterministic because it depends on the congestion in the network. It is random in nature and is described by stochastic parameters. A. Hermoso-Carazo et al. developed a nonlinear filtering algorithm for one-time step and two-time step randomly delayed measurements using the extended and the unscented Kalman filter. Later, Jianrong Wang et al. incorporated the cubature Kalman filter to solve the nonlinear filtering problems with one-step randomly delayed measurement. For a nonlinear system and with one step randomly delayed measurement, quadrature filters are modified to carry out the state estimation efficiently.
