Probabilities, Random Variables, and Random Processes: Digital and Analog |
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Page 440
... Wiener filter to recover the best mean square error approximation of the signal . If such a filter is too difficult to achieve , we may consider a close relative , called a Kalman filter . The name dropping of the terms " matched , " " ...
... Wiener filter to recover the best mean square error approximation of the signal . If such a filter is too difficult to achieve , we may consider a close relative , called a Kalman filter . The name dropping of the terms " matched , " " ...
Page 461
... Wiener filter result is H ( w ) = Sff ( w ) Sff ( w ) + Snn ( w ) ( 9.11 ) Whenever an abstract result such as Eq . 9.10 or 9.11 is encountered , it is a good idea to develop a feeling for it by applying it to trivial cases for which ...
... Wiener filter result is H ( w ) = Sff ( w ) Sff ( w ) + Snn ( w ) ( 9.11 ) Whenever an abstract result such as Eq . 9.10 or 9.11 is encountered , it is a good idea to develop a feeling for it by applying it to trivial cases for which ...
Page 523
... Wiener filter , 458 , 466 Wiener - Khinchine theorem , 347 z transforms , 356 , 373 Zero initial energy , 337 INDEX 523.
... Wiener filter , 458 , 466 Wiener - Khinchine theorem , 347 z transforms , 356 , 373 Zero initial energy , 337 INDEX 523.
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5 | 54 |
General Formulation and Solution of Problems | 69 |
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A₁ autocorrelation function average axiom B₁ balls Chapter convolution correlation integrals cross-correlation cross-correlation function cumulative distribution function dadß defined definition delta function denoted derivation deterministic discrete Fourier transform discrete probability theory discrete random process DRILL SET ensemble ergodic evaluate event space Example finite first-order stationary formula fundamental theorem fxy(a fy(B fz(y given impulse response joint density function joint mass function Laplace transform linear system M₁ member waveform notation obtained otherwise output periodic function periodic waveform permutations plotted in Figure points power spectral density probability measure probability theory problem properties pulse px(a random input random phenomenon range Rxx(T sample description space sampled values second-order stationary set theory shown in Figure shown plotted signal sketch SOLUTION solved statistics Sxx(w Ticket Pays two-sided Laplace transform two-sided z transform typical member X₁ z transform zero-mean