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axis, with a spectral estimate based on a window w
n
+
m
(
t
) of the data centred at
b
n
+
m
on the time axis,
E
S
gg,
n
(
t
)
S
gg,
n
+
m
(
f
)
I
2
E
H
n
(
f
)
H
∗
n
(
f
)
H
n
+
m
(
f
)
H
∗
n
+
m
(
f
)
,
1
=
(2.366)
where
H
n
(
f
)and
H
n
+
m
(
f
) are Fourier transforms of the respective windowed data
segments. Then,
E
S
gg,
n
(
f
)
S
gg,
n
+
m
(
f
)
I
2
∞
∞
∞
∞
1
e
−
i
2π
f
(
s
−
t
+
u
−
v)
=
−∞
−∞
−∞
−∞
·
w
n
(
s
)w
∗
n
(
t
)w
n
+
m
(
u
)w
∗
n
+
m
(v)
·
E
g(
s
)g
∗
(
t
)g(
u
)g
∗
(v)
ds dt dud
v.
(2.367)
The evaluation of this expected value requires the expected value of a fourth moment
of the process generatingg(
t
). The realisations of the process are complex numbers.
If they are taken to have real and imaginary parts that jointly are normally distrib-
uted with zero mean, at a number of points
N
along the time axis, the probability
density has been given by Wooding (1956) as
exp
jk
g
k
,
1
−
g
∗
j
c
−
1
p
(g
1
,g
2
,...,g
N
)
=
(2.368)
N
π
|
C
|
where summation is implied over the repeated subscripts
j
,
k
, while
C
is the Her-
mitian variance-covariance matrix,
is its determinant and
c
−
1
jk
are the elements
of its inverse. Wooding (1956) also gives the characteristic function for this distri-
bution as
|
C
|
exp
4
ω
∗
j
c
jk
ω
k
1
Φ=
−
(2.369)
exp
i
ω
∗
j
g
j
+
ω
j
g
∗
j
/2
p
(g
1
,g
2
,...,g
N
)
d
=
V
,
(2.370)
with the integral over all volume elements
d
of the complex realisation space.
Again, summation is implied over repeated subscripts, and
c
jk
are the elements of
the variance-covariance matrix
C
. Labelling the realisations at locations
s
,
t
,
u
, von
the time axis g
1
, g
2
, g
3
, g
4
, respectively, power series expansion of the exponential
in the integral (2.370) for the characteristic function, and integration term by term,
yield
V
4
pd
1
4!
1
16
···+
ω
∗
1
g
1
+
ω
2
g
∗
2
+
ω
∗
3
g
3
+
ω
4
g
∗
4
+···
Φ=
1
+···+
V+···
1
16
ω
∗
1
g
1
ω
2
g
∗
2
ω
∗
3
g
3
ω
4
g
∗
4
pd
=
1
+···+
V+···
16
ω
∗
1
ω
2
ω
∗
3
ω
4
E
g
1
g
∗
2
g
3
g
∗
4
1
=
1
+···+
+···
,
(2.371)
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