EXTENDED CHANDRASEKHAR RECURSIONS

We extend the discrete-time Chandrasekhar recursions for least-squares estimation in constant parameter state-space models to a class of str uctured time-variant state-space models, special cases of which often arise in adaptive filtering. It can be shown that the much studied exp onentially weighted recursive least-squares filtering problem can be r eformulated as an estimation problem for a state-space model having th is special time-variant structure. Other applications arise in the mul tichannel and multidimensional adaptive filtering context.