## Adaptive Linear Discrete Time Estimation |

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### Contents

INTRODUCTION | |

ITERATION OF THE MOMENTS OF THE ESTIMATES | 12 |

CONCLUSION | 49 |

2 other sections not shown

### Common terms and phrases

absolute summability algorithm 9 amplitude modulated analysis Appendix application assumption of independent constant step constant step-size continually adapting estimators correlation Covariance Matrix CWW(n cww(n+1 data process dd dx dependent data samples dependent samples diagonal terms dx x xd effects of dependent eigenvalue example excess MNSE filtering fundamental assumption Gaussian distributed Gaussian processes Gaussian samples gradient-descent MNSE Griffiths 24 iterative Lemma B-l linear estimate lower bound matrix inverse Mean Norm-Squared Error mean weight matrix mean-squared error minimum mean-squared error minimum MNSE modified GGD algorithm non-stationary obtained optimal weight matrix pilot-matrix algorithm prediction error prediction problem predictor Proof of Lemma Proof of Theorem random step sizes random variables recursion relation scalar shown in Fig skipping samples stationary process statistically independent statistics steady-state misadjustment step-size sequence step-size strategy stochastic approximation symmetric matrix total power upper bound variance weight error density weight-adjustment algorithms xx xd