Sample matrix inversion

Sample matrix inversion (or direct matrix inversion) is an algorithm that estimates weights of an array (adaptive filter) by replacing the correlation matrix $R$ with its estimate. Using $K$ $N$ -dimensional samples $X_{1},X_{2},\dots ,X_{K}$ , an unbiased estimate of $R_{X}$ , the $N\times N$ correlation matrix of the array signals, may be obtained by means of a simple averaging scheme:

{\hat {R}}_{X}={\frac {1}{K}}\sum \limits _{k=1}^{K}X_{k}X_{k}^{H},

where $H$ is the conjugate transpose. The expression of the theoretically optimal weights requires the inverse of $R_{X}$ , and the inverse of the estimates matrix is then used for finding estimated optimal weights.

References[edit]

Widrow, B.; Mantey, P. E.; Griffiths, L. J.; Goode, B. B. (1967). "Adaptive antenna systems" (PDF). Proceedings of the IEEE. 55 (12): 2143–2159. doi:10.1109/proc.1967.6092.
Haykin, S. (2002). Adaptive Filter Theory. Prentice Hall. pp. 165–168. ISBN 0-13-048434-2.