WHITE NOISE ESTIMATION FOR DISCRETE-TIME SYSTEMS WITH RANDOM DELAY AND PACKET DROPOUT

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations.When the random delay is known online,i.e.,time stamped,the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique,and then an optimal input white noise estimator is presented based on the stochastic Kalman filtering theory.However,the optimal white-noise estimator is timevarying,stochastic,and doesn't converge to a steady state in general.Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria.The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator.It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.