  This paper extends the Chandrasekhar-type recursions due to Morf, Sidhu, and
Kailath "Some new algorithms for recursive estimation in constant, linear,
discrete-time systems, IEEE Trans. Autom. Control 19 (1974) 315-323" 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 may have
potential computational advantages over the Kalman Filter and, in particular,
the periodic Riccati difference equation.
