Direct Inversion of the Sample Covariance Matrix
The usefulness of an adaptive array often depends on its convergence rate. For example, when adaptive radars simultaneously reject jamming and clutter while providing automatic platform motion compensation, then rapid convergence to steady-state solutions is essential. Convergence of adaptive sensor arrays using the popular maximum signal-to-noise ratio (SNR) or least mean squares (LMS) algorithms depend on the eigenvalues of the noise covariance matrix. When the covariance matrix eigenvalues differ by orders of magnitude, then convergence is exceedingly long and highly example dependent. One way to speed convergence and circumvent the convergence rate dependence on eigenvalue distribution is to directly compute the adaptive weights using the sample covariance matrix of the signal environment.
Direct Inversion of the Sample Covariance Matrix, Page 1 of 2
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