## Signal processing: the modern approach |

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### Contents

Discrete Signal Analysis | 55 |

Discrete Random Signals | 83 |

Classical Approach | 149 |

Copyright | |

8 other sections not shown

### Common terms and phrases

algorithm aliasing all-pole all-zero analysis analyze approach ARMAX model assume bandpass calculate chapter characterized commands convergence correlation corresponding covariance criterion define determine develop difference equation digital filter discrete signal discrete-time discuss DtFT equivalent exponential FIGURE filter design following example Fourier transform frequency response gaussian given IEEE impulse response innovations input inverse Kalman filter Levinson linear system matrix method model-based MVDR noisy Note obtain optimal orthogonality output parameter estimation performance periodogram poles polynomial power spectrum prediction error prediction-error predictor problem processor properties random signal random variable recursion reflection coefficient relations representation RPEM Ryy(k sampled signal sampling interval sequence shown in Fig signal estimate Signal Processing simulation sinusoidal spectral estimation SSPACK state-space stationary statistical stochastic gradient stochastic processes summarize Suppose Syy(z Table techniques theorem transfer function uncorrelated unit circle values variance vector Wiener filter Wiener solution window Z-transform zero-mean zeros