Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
Improved Quasi-Newton adaptive-filtering algorithm
IEEE Transactions on Circuits and Systems Part I: Regular Papers
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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We study the nonlinear round-off error accumulation system of the conventional recursive least squares algorithm, and we derive bounds for the relative precision of the computations in terms of the conditioning of the problem and the exponential forgetting factor, which guarantee the numerical stability of the finite-precision implementation of the algorithm; the positive definiteness of the finite-precision inverse data covariance matrix is also guaranteed. Bounds for the accumulated round-off errors in the inverse data covariance matrix are also derived. In our simulations, the measured accumulated roundoffs satisfied, in steady state, the analytically predicted bounds. We consider the phenomenon of explosive divergence using a simplified approach; we identify the situations that are likely to lead to this phenomenon; simulations confirm our findings