User`s guide

Burg Method
5-51
Burg Covariance Modified Covariance Yule-Walker
Characteristics
Does not apply
window to data
Does not apply
window to data
Does not apply
window to data
Applies window to
data
Minimizes the
forward and
backward prediction
errors in the
least-squares sense,
with the AR
coefficients
constrained to satisfy
the L-D recursion
Minimizes the
forward prediction
error in the
least-squares sense
Minimizes the
forward and
backward prediction
errors in the
least-squares sense
Minimizes the
forward prediction
error in the
least-squares sense
(also called
“Autocorrelation
method”)
Advantages
High resolution for
short data records
Better resolution than
Y-W for short data
records (more
accurate estimates)
High resolution for
short data records
Performs as well as
other methods for
large data records
Always produces a
stable model
Able to extract
frequencies from data
consisting of p or more
pure sinusoids
Able to extract
frequencies from data
consisting of p or more
pure sinusoids
Always produces a
stable model
Does not suffer
spectral line-splitting
Disadvantages
Peak locations highly
dependent on initial
phase
May produce unstable
models
May produce unstable
models
Performs relatively
poorly for short data
records
May suffer spectral
line-splitting for
sinusoids in noise, or
when order is very
large
Frequency bias for
estimates of sinusoids
in noise
Peak locations
slightly dependent on
initial phase
Frequency bias for
estimates of sinusoids
in noise
Frequency bias for
estimates of sinusoids
in noise
Minor frequency bias
for estimates of
sinusoids in noise
Conditions for
Nonsingularity
Order must be less
than or equal to half
the input frame size
Order must be less
than or equal to 2/3
the input frame size
Because of the biased
estimate, the
autocorrelation
matrix is guaranteed
to positive-definite,
hence nonsingular