## Detection and Estimation Theory and Its Applications |

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### Contents

Review of Probability | 3 |

Stochastic Processes | 35 |

Signal Representations and Statistics | 56 |

Copyright | |

24 other sections not shown

## Detection and Estimation Theory and Its Applications |

We haven't found any reviews in the usual places.

Review of Probability | 3 |

Stochastic Processes | 35 |

Signal Representations and Statistics | 56 |

Copyright | |

24 other sections not shown

algorithm amplitude approximation assumed autocorrelation function average bandpass baseband Bayes binary channel coefficients complex computed conditional correlation corresponding Cramer-Rao criterion crosscorrelation decision rule decision variable defined density function derived detector structure discrete distribution error-rate performance estimation theory example false alarm follows frequency functional block diagram Gaussian noise Gaussian random variables hypothesis Kalman filter last equation likelihood ratio linear log-likelihood log-likelihood ratio M-ary MAP estimate MATLAB measurements minimax ML estimate multipath multiple Neyman-Pearson noise process noncoherent nonlinearity normal number of samples obtained optimal optimum orthogonal output power spectral density priori probabilities probability of detection probability of error Problem Prove Eq pulse RAKE receiver Rayleigh received signal receiver structure Rician Section shown in Figure shows Simulink statistically independent stochastic process theorem threshold transmitted unbiased users values variance a2 vector white Gaussian noise Wiener filter Wilcoxon test written zero zero-mean