Suboptimal white noise estimators for discrete time systems with random delays

This paper is concerned with the suboptimal deconvolution problems for discrete-time systems with random delayed observations and data losses. 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 a 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. The obtained estimator is indeed a fixed-point smoother, based on which a fixed-lag white-noise smoother is derived. Further, it can be shown that the suboptimal input white-noise estimators converge to the steady-state ones under appropriate assumptions.

► This paper studies the suboptimal deconvolution problems for systems with multiple random observation delays and data losses.
► The random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique.
► A suboptimal input white-noise estimator with deterministic gains is developed under new criteria.
► The suboptimal input white-noise estimator converges to a stationary white-noise estimator under appropriate assumptions.