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bartletts method is used to estimate the power spectrum of a process from a sequence of n 2000 samplesa what is the
in section 643 we presented a recursion for converting the direct-form coefficients bqk into the lattice filter
consider the signal xn n lun for n 0 1 na find the first-order model and the minimum error using the covariance
write an m-file for finding the model for a signal using the method proposed by itakura see section 653 compare the
suppose that a process xn has been recorded but there is a missing gap of data over the interval n1 n2 ie xn is unknown
suppose that a signal xn is recorded and that due to measurement errors there are outliers in the data ie for some
modify the mfile rt og m to find the cholesky ldu decomposition of a hermitian toeplitz matrix rx compare the
the schur recursion is a mapping from the autocorrelation sequence rxk to a set of reflection coefficientsa write a
design a two-pole lattice filter that has poles at rejthetanbspand re-jthetanbspand draw a carefully labeled flow graph
consider the signalsuppose that we observe xn for n 0 i na using the autocorrelation method find the 2nd-order
which of the following autocorrelation sequences are extendible for those that are extendible find an extension and
the reflection coefficients corresponding to a third-order all-pole model are and the modeling error is given bya find
the reflection coefficients for a two-pole model of a signal xn are r1nbsp 025 and r2nbsp 025 and the modeling error is
you are told that it is always possible to determine whether or not a causal all-pole filter is stable from a finite
if one is modeling a signal xn whose transform xz contains zeros then an all-pole model may be used to effectively
if rx0 1 rx1 05 and rx2 075 find the values of a1 a2 and b0 in the following ar2 model for xn xn a1xn - 1 a2xn - 2
use the method of spectral factorization to find a moving average model of order 2 for a process whose autocorrelation
in this problem you are to consider the design of low pass filters using the pade and prony methodsa suppose you would
given the autocorrelation sequencerx0 1 rx1 08 rx2 05 rx3 01find the reflection coefficients j the model parameters
equation 4129 may be used to reduce the amount of computation required to set-up the covariance normal equationsa show
find the power spectrum for each of the following wide-sense stationary random processes that have the given
suppose we are given a zero mean process xn with autocorrelationa find a filter which when driven by unit variance
for each of the following determine whether or not the random process isi wide-sense stationaryii mean ergodica xn a
determine which of the following correspond to a valid autocorrelation sequence for a wss random process for those that
for each of the following determine whether the statement is true or falsea all wide-sense stationary moving average