A random process is the surn of a deterministic sinusoid and WGN, given as X[n] =...
and is X(t) a WSS process?
6.11 Sinusoid with random phase. Consider a random process x(t)-A cos(wot + ?), where wo are nonrandom positive constants and o is a RV uniformly distributed over A and (0, ?), i.e., ? ~11(0, ?). (a) Find the mean function 2(t) of X(t).
A stochastic process X(t) is defined via: X(t,w) = A(w)t + Bw), te 1-1, 1], where Aw) ~ U([-1,1]) and B(w) ~ U((-1,1]) are statistically independent random variables. For this process: 2.a) plot two sample realizations x1(t) and x2(t). 2.b) Determine the first-order PDF fx(x;t) associated with it. 2.c) Determine the mean pz(t) and variance ož(t). 2.d) Determine the autocorrelation Rex(ti, t2) and the auto-covariance Cxx(t1, t2) associated with it.
/lay Figure 9.1: Discrete-time sinusoid sin 0.11 n and its Fourier spectra. x[n] = sin Olan= (010.17n – e-O.lan) (9.15) From the spectra in Fig. 9.1 write the Fourier series corresponding to the interval - 10 2r> -30 (or-T2N>-37). Show that this Fourier is equivalent to that in Eq. (9.15).
Problem 5: Noisy Signal A signal generator generates a random sinusoid, X cos (2nt + Θ) whose amplitude is given by a random variable X uniformly distributed between-1 and 1, and phase Θ is an independent random variable which takes each of the following values π 0, π with equal prob- ability. This signal's amplitude is additively corrupted by independent noise YN(0, 0.01) The output amplitude is denoted by Z, where Z-X +Y. Assuming that an estimator of X has...
matlab
Problem 6: The Matlab command 'randn(m,n) produces an m x n matrix of random numbers that are a realization of a white random process with some probability density function. Moreover, the Matlab command 'rand(m,n)' produces an mxn matrix of random numbers between 0 and 1 that are a realization of a white random process with some probability density function. a) Use Matlab to do the following steps: 1) Let u-randn (10000,1); and plot u. 2) Use the command 'mean(u)'...
The random process X(t) is defined by X(t) = X cos 27 fot + Y sin 2 fot, where X and Y are two zero-mean Gaussian random variables, each with the variance 02. (a) Find ux(t) (b) Find RX(T). Is X(t) stationary? (c) Repeat (a) and (b) for 0 + 0
Problem 8.2 Suppose that Xi, X,.., Xn is a random sample of size n is to be taken from a population with pdf 2 In>X (In2) x We are interested in determining the approximate distribution of the sample geometric mean given by [x. If we let Y-In X, then we can re-express the geometric mean as a) Determine the mean of Y. Hint, if u = In x, then du = 1/x dx. b) Determine the variance of Y. c)...
Consider a random vector X e RP with mean EX is a p x p dimensional matrix. Denote the jth eigenvalue and jth eigenvector of as and øj, respectively. 0 and variance-covariance matrix Cov[X] = . Note that Define the random score vector Z as Х,Ф — Z where is the rotation matrix with its columns being the eigenvectors 0j, i.e., | 2|| Ф- Perform the following task: Show that the variance-covariance matrix of random score vector Z is ....
A random process is generated as follows: X(t) = e−A|t|, where A is a random variable with pdf fA(a) = u(a) − u(a − 1) (1/seconds). a) Sketch several members of the ensemble. b) For a specific time, t, over what values of amplitude does the random variable X(t) range? c) For a specific time, t, find the mean and mean-squared value of X(t). d) For a specific time, t, determine the pdf of X(t).
Consider the random sequence {x [n]} characterized by the following difference equation x[n + 1] = - (n+1) n20 x[n] ERM Let x(0) be a random vector with mean (0) and convariance matrix CO). Determine the mean value function (n), the covariance kernel Cz(k,j), and the covariance matrix Cy(k) fort this process.