ADAPTIVE ESTIMATION WITH MUTUALLY CORRELATED TRAINING SAMPLES.
STANFORD UNIV CALIF STANFORD ELECTRONICS LABS
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The linear least-mean-square estimate of one element from a sequence of scalar random variables given an observation of the corresponding element from a sequence of vector-valued random variables data is well known. Computation of the estimate requires knowledge of the data correlation matrix. Algorithms have been proposed for iterative determination of the estimate when the data correlation matrix is unknown. These algorithms are easy to implement, require little storage, and are suitable for real-time processing. Past convergence studies of these algorithms have assumed that the data vectors were mutually independent. Author
- Operations Research