## Random signals, estimation, and identification: analysis and applications |

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2nfct algorithm autocorrelation called characteristic function coefficient Compute covariance function covariance matrix cross cross covariance defined density function differential equation discrete distribution eigenvalue entropy example Find finite Fourier transform Gaussian process given by Eq given by X(t Hence independent innovation process input integral interval Kalman filter known Let the received Let us denote linear system Markov chain Markov process mean square estimator method minimum nonlinear Note observed obtain optimum orthogonal output parameter periodogram Poisson process positive definite power spectral density probability density process X(t random process random signal random variables received signal RLC circuit Rx(x samples sequence Show shown in Fig sides of Eq signal X(t Solution space stationary process stochastic process Sx(f system is given theorem transfer function unbiased estimator uncorrelated values variance a2 vector W(fc white noise Wiener process X(fc X(tf Y(fc yields zero zero-mean white