Abstract
Sayed and Kailath (1994) demonstrated the feasibility of directly deriving many known adaptive filtering algorithms in square-root forms by a proper reformulation of the original adaptive problem into a state-space form. This work employs this state-space form to develop adaptive interpolation and smoothing algorithms. In particular, a systematic and concise derivation of the QR-decomposition least-squares lattice (QRD-LSL) interpolation and smoothing algorithms using correspondences between Kalman filtering and LSL adaptive filtering is given.
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