Kleptsyna, M. L.; Kloeden, P. E.; Anh, V. V. Linear filtering with fractional Brownian motion in the signal and observation processes. (English) Zbl 0930.93075 J. Appl. Math. Stochastic Anal. 12, No. 1, 85-90 (1999). The authors consider the linear problem with the signal \(\theta _{t}\) and the observation \(\xi _{t}\) defined by the linear equations \[ \theta _{t}=\int_{0}^{t}a(s)\theta _{s}ds+B_{t}^{h},\quad \xi _{t}=\int_{0}^{t}A(s)\theta _{s}ds+W_{t}+B_{t}^{h}, \] where \(B_{t}^{h}\) is a fractional Brownian motion with Hurst index \(h\in (3/4,1)\) and \(W_{t}\) is a standard Wiener process. For \(\hat{\theta}_{t}=E(\theta _{t}|\xi _{s},0\leq s\leq t)\) they obtain the expression \(\hat{\theta}_{t}=\int_{0}^{t}\Phi (t,s)d\xi _{s}\) where \(\Phi \) can be found from an integral equation derived in the paper. Reviewer: Grigori Milstein (Ekaterinburg) Cited in 4 Documents MSC: 93E11 Filtering in stochastic control theory 60G20 Generalized stochastic processes 60G35 Signal detection and filtering (aspects of stochastic processes) Keywords:fractional Brownian motion; optimal mean-square filter; theorem on normal correlation PDFBibTeX XMLCite \textit{M. L. Kleptsyna} et al., J. Appl. Math. Stochastic Anal. 12, No. 1, 85--90 (1999; Zbl 0930.93075) Full Text: DOI EuDML