## Signal Detection and EstimationThis newly revised edition of a classic Artech House book provides you with a comprehensive and current understanding of signal detection and estimation. Featuring a wealth of new and expanded material, the second edition introduces the concepts of adaptive CFAR detection and distributed CA-CFAR detection. The book provides complete explanations of the mathematics you need to fully master the material, including probability theory, distributions, and random processes. |

### What people are saying - Write a review

User Review - Flag as inappropriate

An excellent book that explains the fundamentals very well

User Review - Flag as inappropriate

It is a book dealing with the basic concepts of signal detection and estimation theory in a very simple language. Best for beginners.

### Contents

Distributions | 75 |

Random Processes | 141 |

DiscreteTime Random Processes | 223 |

Copyright | |

10 other sections not shown

### Other editions - View all

### Common terms and phrases

assumed autocorrelation function Bayes binary detection CFAR Chapter characteristic function clutter coefficients conditional density function Consider constant correlation corresponding covariance matrix decision regions decision rule defined denoted detector Determine diagonal distribution function eigenfunctions eigenvalues eigenvectors Example false alarm Fourier transform frequency Gaussian random variables Hence impulse response integral equation interval joint density function likelihood ratio test linear Markov chain matched filter mean and variance mean zero minimum mean-square error minimum probability noise process Note observation obtain optimum receiver orthogonal output parameter PN sequence power spectral density probability density function probability of detection probability of error probability of false problem radar random process X(t received signal sample function sequence shown in Figure Solution solve spread spectrum statistically independent Substituting sufficient statistic target theorem unbiased variance a2 vector white balls white Gaussian noise wide-sense stationary Wiener process zero mean