Random Signal Processing
Including coverage of Wiener filtering and Kalman filtering, this text presents a treatment of estimation theory and a look at detection (or template matching) theory for communication and pattern. It also provides current, applications-oriented coverage of entropy.
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autocorrelation function average basis vectors binary calculate characteristic function Chebyshev's inequality codomain concept continuous-time convolution correlation coefficient correlation function define definition derive determined discrete discrete-time signals distribution domain dot product eigenvalues eigenvectors elements energy signal entropy equal equations error orthogonal EXAMPLE experiment experimental outcome Find and plot formula Fourier transform frequency Fx(a Gaussian random variable given gives impulse response independent inner product input signal large numbers least-squares linear mean-square error mean-square estimation method multiply number of heads optimum filter orthonormal output parameters periodic power signals power spectrum probability properties pseudoinverse random number random signals real numbers Repeat Problem result Review sample space sequence shown in Fig shows solution solve spectral density function squared errors stationary statistically independent statistics Step stochastic process subset Suppose template toss a coin transfer function uncorrelated variance vector space waveform white noise zero