New design of linear least-squares fixed-interval smoother using covariance information
Copyright policy )New design of linear least-squares fixed-interval smoother using covariance information
Abstract This paper derives recursive linear least-squares fixed-interval smoothing algorithm using covariance information from a Wiener-Hopf integral equation. The algorithm is obtained for the white plus coloured and white Gaussian observation noises. Autocovariance functions of the signal and the coloured noise are expressed using a degenerate kernel. The degenerate kernel can represent general covariance functions of nonstationary or stationary processes by a finite sum of nonrandom functions. The efficiency of the smoother was assured by a numerical example.
white plus coloured observation noise, nonstationary stochastic signal process, Linear systems in control theory, Wiener-Hopf integral equation, autocovariance functions, degenerate kernel, Data smoothing in stochastic control theory, covariance information, recursive linear least-squares fixed-interval smoothing algorithm, invariant imbedding method
white plus coloured observation noise, nonstationary stochastic signal process, Linear systems in control theory, Wiener-Hopf integral equation, autocovariance functions, degenerate kernel, Data smoothing in stochastic control theory, covariance information, recursive linear least-squares fixed-interval smoothing algorithm, invariant imbedding method
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