Sayed, Ali H.Kailath, T.2017-12-192017-12-192017-12-19199410.1109/9.280773https://infoscience.epfl.ch/handle/20.500.14299/142906We extend the discrete-time Chandrasekhar recursions for least-squares estimation in constant parameter state-space models to a class of structured time-variant state-space models, special cases of which often arise in adaptive filtering. It can be shown that the much studied exponentially weighted recursive least-squares filtering problem can be reformulated as an estimation problem for a state-space model having this special time-variant structure. Other applications arise in the multichannel and multidimensional adaptive filtering context.text::journal::journal article::research article