Abstract
This Note extends the Chandrasekhar-type recursions due to Morf, Sidhu, and Kailath (1974) to the case of periodic time-varying state-space models. We show that the S-lagged increments of the one-step prediction error covariance satisfy certain recursions from which we derive some algorithms for linear least squares estimation for periodic state-space models. The proposed recursions have potential computational advantages over the Kalman Filter and, in particular, the periodic Riccati difference equation. To cite this article: A. Aknouche, F. Hamdi, C. R. Acad. Sci. Paris, Ser. I 346 (2008).
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