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Classical Detection and Estimation Theory
Representations of Random Processes
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analog approximate assume assumption Bayes binary block diagram bound channel Chapter coefficients colored noise Consider correlation corresponds covariance function covariance matrix decision space defined denote derive detection problem differential equation discussion eigenfunctions eigenvalues energy equal estimation problem estimation theory expression finite frequency Gaussian process Gaussian random variable hypothesis impulse response input integral equation interest Ka(t known signal likelihood ratio test linear filter linear modulation MAP estimate mean-square error minimize MMSE nonlinear nonrandom obtain optimum linear filter optimum receiver orthogonal signals output parameter performance phase posteriori probability density probability of error Property radar realizable received signal received waveform result sample function scalar Section shown in Fig simple example solution solve spectral height spectrum stationary processes statistically independent sufficient statistic techniques threshold transform transmitted unrealizable filter vector Verify waveform white noise whitening filter zero zero-mean Gaussian