Digital Signal Processing Reference
In-Depth Information
rapidly (depending on N ) k ln ( N ). This criterion is named MDL (Minimum
Description Length):
()
() ()
MDL
kN
=
1n
ˆ
+
kN
ln
[6.35]
ρ
k
This criterion is consistent and gives better results than AIC [WAX 85].
It would be tedious to present all the criteria that were developed; for more
information on this, see the following references: [BRO 85, BUR 85, FUC 88, PUK
88, YIN 87, WAX 88].
[WAX 85] expressed the AIC and MDL criteria depending on the eigenvalues of
the autocorrelation matrix
ˆ R :
1
p
pk
ˆ
λ
t
tk
=+
1
() ( )
(
)
AIC k
=
N
p
k
ln
+
k
2
p
k
k
=
1,
,
p
1
[6.36]
p
ˆ
1
λ
t
pk
tk
=+
1
1
p
pk
ˆ
λ
t
1
tk
=+
1
() ( )
(
) ( )
MDL k
=
N
p
k
ln
+
k
2
p
k
ln
N
[6.37]
p
2
ˆ
1
λ
t
pk
tk
=+
1
ˆ λ are the ordered eigenvalues (
)
ˆ
ˆ
ˆ y Rp × . It is
possible to define these criteria according to the singular values ˆσ of the matrix
(
of the matrix (
)
where
λλ +
t
t
1
ˆ
ˆ
)
2
y RMpM p
×
,
>
by replacing
λ σ in the matrices [6.36] and [6.37]
by
ˆ
l
t
[HAY 89].
The increase in the number of lines of the matrix ˆ R evidently makes an
improvement of the order estimation performances possible. We have compared the
two expressions [6.35] and [6.37] of the MDL criterion on the following example.
We consider N = 100 samples of a signal made up of two sinusoids of identical
amplitudes of frequencies 0.1 and 0.2 and of one white noise. The dimension of the
matrix ˆ R is (30 x 30). The simulations were carried out for signal-to-noise ratios of
10 dB and 0 dB and are presented in Figures 6.3 and 6.4.
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