Detection, Estimation, and Modulation Theory: Detection, estimation, and linear modulation theory |
Contents
Contents | 1 |
Classical Detection and Estimation Theory | 15 |
Representations of Random Processes | 166 |
Copyright | |
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analog assume Bayes bound channel Chapter characterization coefficients colored noise consider correlation covariance function covariance matrix decision space defined denote derive detection problem differential equation discussion eigenfunctions eigenvalues energy estimation problem estimation theory expression finite frequency Gaussian process Gaussian random variable H₁ ho(t hypothesis impulse response infinite input integral equation kernel known signal likelihood function likelihood ratio test linear filter MAP estimate mean-square error modulation nonlinear observation interval obtain optimum receiver orthogonal signals output P₁ parameter performance phase probability density probability of error processor Property r₁ radar realizable received signal received waveform result s₁(t Sa(w sample function Section shown in Fig Sn(w solution solve spectral height spectrum stationary stationary processes statistically independent sufficient statistic T₁ techniques Theory transform transmitted unrealizable variance vector waveform white noise whitening filter zero zero-mean