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An
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Dir
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Hy
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CC B
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m
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1.
I
NT
RO
D
UCT
I
O
N
An
g
le
o
f
a
r
r
iv
al
(
AOA)
esti
m
atio
n
is
a
p
ar
t
o
f
ch
an
n
el
e
s
tim
atio
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p
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ce
d
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wh
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in
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th
e
s
ig
n
als
in
th
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wir
eless
co
m
m
u
n
icatio
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p
ar
ad
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m
.
B
ea
m
f
o
r
m
in
g
a
p
p
r
o
ac
h
es
ar
e
u
s
ed
f
o
r
AOA
esti
m
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n
in
an
ten
n
a
co
m
m
u
n
icatio
n
s
y
s
tem
s
.
Acc
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in
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t
h
e
e
s
tim
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o
f
AOA
is
p
er
f
o
r
m
ed
,
h
ig
h
lig
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ts
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
a
n
ten
n
a
co
m
m
u
n
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im
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lem
en
tatio
n
.
Dir
ec
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n
o
f
t
h
e
s
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al
ap
p
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ch
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h
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te
n
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o
r
tan
t
in
b
o
th
th
e
r
a
d
ar
an
d
s
o
n
ar
ap
p
licatio
n
s
[1
]
-
[
3
]
.
T
r
ad
itio
n
al
m
et
h
o
d
s
l
ik
e
m
u
s
ic
[
1
]
an
d
esti
m
atio
n
o
f
s
ig
n
al
p
ar
am
eter
s
v
ia
r
o
tatio
n
al
in
v
ar
ian
t
tec
h
n
iq
u
es
(
E
SP
R
I
T
)
[
2
]
ar
e
ca
p
ab
le
o
f
f
in
d
in
g
th
e
AOA
o
f
N
-
1
s
ig
n
a
ls
wh
ile
N
elem
en
t
u
n
if
o
r
m
lin
ea
r
ar
r
a
y
is
u
s
ed
.
W
h
ile
th
e
n
u
m
b
er
o
f
s
o
u
r
ce
s
th
at
ar
e
r
eso
lv
ed
is
less
th
an
th
e
n
u
m
b
er
o
f
s
en
s
o
r
s
u
s
ed
th
e
n
t
h
e
p
r
o
b
lem
is
d
ef
in
e
d
as
u
n
d
er
d
eter
m
in
e
d
AOA
esti
m
atio
n
d
is
cu
s
s
ed
in
[
4
]
-
[
6
]
.
T
h
ese
u
n
d
er
d
eter
m
in
ed
a
p
p
r
o
ac
h
es
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s
e
th
e
d
eg
r
ee
o
f
f
r
ee
d
o
m
(
DOF)
b
y
cr
ea
tin
g
a
v
ir
tu
al
ar
r
ay
[
5
]
.
C
o
m
p
letely
d
ef
i
n
ed
n
o
n
-
u
n
if
o
r
m
lin
ea
r
ar
r
ay
a
n
d
th
e
s
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n
al
r
ec
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ed
o
n
th
at
a
r
r
ay
is
u
s
ed
to
g
en
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th
e
v
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tu
al
ar
r
ay
.
A
co
v
ar
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n
ce
m
atr
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is
g
en
er
ated
f
r
o
m
th
e
n
o
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-
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n
if
o
r
m
lin
ea
r
ar
r
ay
an
d
th
e
r
ec
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d
ata.
Vec
to
r
izin
g
th
at
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ian
ce
m
atr
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x
g
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th
e
v
ir
tu
al
a
r
r
ay
.
Su
ch
a
n
ar
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ay
ca
lled
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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d
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J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
1
,
Octo
b
er
2021
:
36
7
-
37
5
368
m
in
im
u
m
r
ed
u
n
d
a
n
cy
ar
r
a
y
(
MRA)
[
7
]
is
g
en
er
ated
in
tr
o
d
u
cin
g
th
e
ap
er
tu
r
e
th
at
is
m
ax
im
u
m
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s
s
ib
le
f
o
r
N
s
en
s
o
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s
.
Desig
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o
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MR
As ca
n
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t b
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p
r
ed
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alth
o
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g
h
a
M
R
A
f
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N≤
1
7
s
en
s
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s
ar
e
cr
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t
ed
in
[
8
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.
R
ed
u
cin
g
th
e
n
u
m
b
e
r
o
f
th
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s
en
s
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s
f
o
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n
d
e
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m
in
e
d
en
v
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n
m
en
t is d
is
cu
s
s
ed
in
r
esear
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p
u
b
licatio
n
s
[9
]
-
[
2
5
]
.
L
ar
g
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ap
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tu
r
e
v
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tu
al
a
r
r
ay
o
r
c
o
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p
r
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ar
r
ay
ar
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u
s
ed
f
o
r
th
e
AOA
esti
m
atio
n
f
o
r
a
n
u
n
d
er
d
eter
m
in
ed
s
y
s
tem
with
n
o
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lin
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u
n
if
o
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m
lin
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ar
r
ay
(
NUL
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with
th
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u
s
e
o
f
a
less
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n
u
m
b
er
o
f
s
en
s
o
r
s
[
5
]
,
[
2
5
]
.
Min
im
u
m
h
o
le
ar
r
ay
(
MH
A)
[
9
]
is
an
o
th
er
m
eth
o
d
f
o
r
v
ir
tu
al
s
en
s
o
r
s
o
r
ar
r
a
y
s
in
th
e
u
n
d
er
d
eter
m
i
n
ed
s
y
s
tem
.
Sin
ce
th
er
e
is
n
o
cl
o
s
ed
f
o
r
m
m
et
h
o
d
f
o
r
th
e
AOA
esti
m
atio
n
an
ex
te
n
s
iv
e
s
ea
r
ch
m
eth
o
d
is
u
s
ed
f
o
r
th
e
p
h
y
s
ical
ar
r
ay
s
.
Sp
ar
s
e
ar
r
ay
s
ar
e
th
e
r
ec
en
t w
ay
s
o
f
im
p
r
o
v
in
g
th
e
s
ea
r
ch
m
eth
o
d
in
th
e
u
n
d
er
d
eter
m
in
e
d
s
y
s
tem
.
Nested
ar
r
ay
s
as
d
is
c
u
s
s
ed
in
[
5
]
co
n
ca
ten
ate
two
u
n
if
o
r
m
lin
ea
r
ar
r
ay
s
(
UL
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to
r
eso
lv
e
N
s
q
u
ar
e
AOA
f
r
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m
N
p
h
y
s
ical
s
en
s
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r
s
.
NUL
A
s
tr
u
ctu
r
es
with
co
p
r
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ay
s
o
b
tain
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b
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h
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in
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in
ter
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r
s
p
ac
in
g
o
f
M
an
d
N
[
1
0
]
,
wh
er
e
M
an
d
N
ar
e
co
p
r
im
e
.
M+
N
-
1
s
en
s
o
r
s
ar
e
n
ee
d
ed
to
r
eso
lv
e
Mx
N
s
o
u
r
ce
s
.
Similar
ly
m
o
r
e
tar
g
ets
u
s
in
g
less
er
s
en
s
o
r
s
ca
n
b
e
r
eso
lv
ed
u
s
in
g
th
e
co
p
r
im
e
ar
r
a
y
with
d
is
p
lace
d
s
u
b
ar
r
ay
s
(
C
ADiS)
[
1
1
]
.
Dif
f
e
r
en
t
AOA
es
ti
m
atio
n
alg
o
r
ith
m
s
a
r
e
d
ef
in
ed
[
1
2
]
-
[
2
2
]
th
at
u
s
es
less
er
s
en
s
o
r
an
d
s
p
ar
s
e
ar
r
ay
s
f
o
r
r
eso
lv
in
g
h
ig
h
e
r
n
u
m
b
er
o
f
s
ig
n
als
ar
e
d
etailed
.
No
n
c
ir
cu
lar
s
ig
n
als
u
s
ed
f
o
r
th
e
AOA
esti
m
atio
n
alg
o
r
ith
m
[
1
4
]
-
[
1
6
]
ex
h
ib
ited
b
etter
p
er
f
o
r
m
a
n
ce
wh
ile
r
ed
u
ci
n
g
th
e
n
o
is
e
[
1
4
]
.
Sp
ar
s
e
B
ay
esian
lear
n
in
g
b
ased
alg
o
r
ith
m
s
ar
e
im
p
lem
e
n
ted
f
o
r
th
e
m
u
ltip
le
m
ea
s
u
r
e
m
en
t
v
ec
to
r
(
MM
V)
s
p
ar
s
e
s
ig
n
al
r
ec
o
v
e
r
y
p
r
o
b
le
m
[
2
6
]
.
E
x
ten
s
io
n
o
f
t
h
is
SS
R
alg
o
r
ith
m
is
d
o
n
e
b
y
b
l
o
ck
s
p
ar
s
e
s
ig
n
al
r
ec
o
v
er
y
p
r
o
b
lem
i
n
[
2
7
]
-
[
2
9
]
b
y
c
o
n
s
id
er
in
g
th
e
tem
p
o
r
al
co
r
r
elatio
n
o
f
s
o
u
r
ce
s
.
Of
f
g
r
id
er
r
o
r
b
ased
alg
o
r
ith
m
s
f
o
r
AOA
es
t
im
atio
n
ar
e
in
tr
o
d
u
c
ed
in
[
3
0
]
.
B
ay
esian
m
o
d
el
wh
ich
d
ef
in
ed
th
e
o
f
f
g
r
id
e
r
r
o
r
as
th
e
p
r
io
r
is
d
ef
in
ed
.
N
o
n
n
e
g
ativ
e
p
r
io
r
in
f
o
r
m
atio
n
is
in
co
r
p
o
r
ated
f
o
r
t
h
e
s
p
ar
s
e
B
ay
esian
lear
n
in
g
al
g
o
r
ith
m
[
3
1
]
.
T
o
en
h
a
n
ce
th
e
ac
cu
r
ac
y
o
f
AOA
esti
m
atio
n
,
p
ar
am
etr
ic
ch
an
g
es
ar
e
m
ad
e
in
th
e
n
o
n
n
eg
ativ
e
s
p
ar
s
e
B
ay
esian
lear
n
in
g
(
N
NSB
L
)
alg
o
r
ith
m
.
T
h
is
p
ap
er
attem
p
ts
in
cr
ea
s
in
g
th
e
a
cc
u
ar
ac
y
o
f
AOA
esti
m
atio
n
b
y
r
ep
lacin
g
th
e
o
v
er
co
m
p
lete
b
asis
v
ec
to
r
o
f
NNSB
L
alg
o
r
ith
m
b
y
th
e
cu
m
u
lat
iv
e
an
d
th
e
h
y
b
r
i
d
b
asis
v
ec
to
r
.
An
ten
n
a
r
ec
o
n
f
i
g
u
r
atio
n
u
s
in
g
t
h
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PSO
)
alg
o
r
ith
m
is
also
ap
p
lied
f
o
r
b
etter
p
e
r
f
o
r
m
an
ce
.
T
h
e
o
b
tain
ed
r
esu
lts
ar
e
co
m
p
ar
ed
u
s
in
g
mean
sq
u
a
re
e
rro
r
(
MSE
)
v
/s
sig
n
a
l
t
o
n
o
ise
ra
ti
o
(
SNR
)
an
d
MSE
v
/s
s
n
ap
s
h
o
t
p
lo
ts
.
Sectio
n
2
d
ea
ls
with
th
e
im
p
lem
en
tatio
n
d
etai
ls
o
f
th
e
m
eth
o
d
o
lo
g
y
ca
r
r
ied
o
u
t.
Sectio
n
3
d
is
cu
s
s
es
o
p
tim
izatio
n
alg
o
r
ith
m
im
p
lem
en
ted
f
o
r
a
n
ten
n
a
r
ec
o
n
f
ig
u
r
atio
n
a
n
d
s
ec
tio
n
4
d
elib
er
ates o
n
th
e
r
esu
lt a
n
d
d
is
cu
s
s
io
n
o
f
th
e
im
p
lem
e
n
tatio
n
an
d
f
o
llo
wed
b
y
c
o
n
clu
s
io
n
an
d
r
ef
er
en
ce
.
2.
H
YB
RID
B
AS
I
S
V
E
C
T
O
R
B
AS
E
D
UND
E
RD
E
T
E
R
M
I
NE
D
AO
A
E
ST
I
M
AT
I
O
N
No
n
n
e
g
ativ
e
s
p
ar
s
e
B
a
y
es
ian
lear
n
in
g
(
NNSB
L
)
f
o
r
u
n
d
e
r
d
eter
m
in
ed
AOA
est
im
atio
n
is
im
p
lem
en
ted
with
th
e
h
y
b
r
id
b
asis
v
ec
to
r
b
ased
alg
o
r
ith
m
.
2
.
1
.
S
pa
rse
re
presenta
t
io
n:
intr
o
du
ct
io
n
Sin
ce
th
e
r
esear
ch
is
b
as
ed
o
n
b
asis
p
u
r
s
u
it
d
e
n
o
is
in
g
(
B
PD
N)
b
ased
s
p
ar
s
e
r
e
p
r
esen
tatio
n
d
ir
ec
tio
n
o
f
ar
r
i
v
al
(
DOA
)
esti
m
atio
n
m
eth
o
d
s
an
in
tr
o
d
u
ctio
n
ab
o
u
t
th
is
m
eth
o
d
is
m
ad
e.
T
h
e
ad
v
an
ce
m
e
n
t
in
th
e
ex
is
tin
g
DOA
esti
m
atio
n
alg
o
r
ith
m
i
n
s
p
ar
s
e
r
ep
r
esen
tati
o
n
p
ar
ad
ig
m
is
ap
p
lied
a
n
d
co
m
p
ar
ed
with
t
h
e
tr
ad
itio
n
al
m
eth
o
d
.
T
h
e
B
PDN
b
ased
DOA
esti
m
atio
n
alg
o
r
ith
m
th
u
s
d
ev
elo
p
ed
i
n
th
e
ex
is
tin
g
liter
atu
r
e
is
ad
v
an
ce
d
with
th
e
cu
m
u
lativ
e
b
asis
v
ec
to
r
an
d
h
y
b
r
i
d
b
asis
v
ec
to
r
-
b
ased
im
p
lem
en
tatio
n
.
2
.
2
.
Sig
na
l
m
o
del
Om
n
id
ir
ec
tio
n
al
a
n
ten
n
as
wit
h
M
elem
en
ts
ar
e
p
lace
d
in
a
n
o
n
-
u
n
if
o
r
m
lin
ea
r
ar
r
a
y
wh
ich
ar
e
lo
ca
ted
at
d
if
f
er
en
t
d
is
tan
ce
s
[
0
,
d
1
,
…
d
M
-
1
]
,
wh
ich
d
en
o
tes
d
is
tan
ce
b
etwe
en
th
e
r
ef
e
r
en
ce
lo
ca
tio
n
an
d
d
if
f
er
en
t
an
ten
n
as.
T
h
is
d
is
tan
ce
is
th
e
i
n
teg
r
al
m
u
ltip
les
o
f
h
alf
th
e
wav
elen
g
t
h
.
I
m
p
r
o
v
em
e
n
t
o
f
co
n
v
er
g
en
ce
in
a
n
y
s
p
ar
s
e
r
e
p
r
esen
tatio
n
p
r
o
b
lem
is
im
p
r
o
v
ed
b
y
in
cr
ea
s
in
g
th
e
d
eg
r
ee
o
f
f
r
ee
d
o
m
(
DOF)
.
DOF
co
n
s
id
er
ed
in
th
e
o
m
n
id
i
r
ec
tio
n
al
an
ten
n
a
ar
r
a
y
is
th
e
d
if
f
er
en
ce
c
o
-
ar
r
ay
d
e
f
in
ed
as
:
=
{
1
−
2
}
1
=
0
,
1
,
…
…
−
1
,
;
2
=
0
,
1
,
…
.
−
1
Fo
r
M
an
te
n
n
as
p
r
o
v
id
es
m
o
r
e
DOFs
.
C
o
n
s
id
er
in
g
t
h
at
N
f
ar
-
f
iled
s
o
u
r
ce
s
u
n
co
r
r
elate
d
in
n
atu
r
e
is
f
allin
g
o
n
M
an
ten
n
as.
T
h
e
n
ar
r
o
w
b
a
n
d
s
o
u
r
ce
s
is
d
ef
in
ed
b
y
k
(
)
,
=
1
,
2
,
…
,
wh
ich
im
p
in
g
es
o
n
an
ten
n
a
a
r
r
ay
s
.
T
h
e
p
r
o
p
o
s
ed
im
p
lem
en
tatio
n
ca
lcu
lates
th
e
DOA
esti
m
atio
n
with
s
p
atia
lly
wh
ite
Gau
s
s
ian
n
o
is
es
as
th
e
ch
an
n
el
f
o
r
all
th
e
M
an
ten
n
as
d
e
n
o
ted
b
y
(
)
,
=
0
,
1
,
…
.
,
−
1
.
T
h
e
s
n
ap
s
h
o
ts
o
f
th
e
s
ig
n
al
with
n
o
is
e
is
d
ef
in
ed
as
:
(
)
=
(
)
+
(
)
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Hyb
r
id
b
a
s
is
ve
cto
r
b
a
s
ed
u
n
d
erd
etermin
ed
b
ea
mfo
r
min
g
a
l
g
o
r
ith
m
in
o
p
timiz
ed
…
(
K
r
u
p
a
P
r
a
s
a
d
K
.
R
.
)
369
Ar
r
ay
r
ec
eiv
ed
v
ec
to
r
x
(
t)
,
s
ig
n
al
f
r
o
m
th
e
tr
a
n
s
m
itti
n
g
s
o
u
r
ce
s
(
t)
an
d
th
e
n
o
is
e
in
th
e
ch
an
n
el
n
(
t)
f
o
r
th
e
ℎ
s
n
ap
s
h
o
t
is
d
en
o
ted
in
(
1
)
.
T
h
e
s
teer
in
g
v
ec
to
r
s
o
f
all
th
e
N
s
o
u
r
ce
s
ar
e
co
n
s
o
lid
ated
in
th
e
m
an
if
o
ld
m
at
r
ix
A
.
=
[
(
1
)
,
(
2
)
,
⋅
⋅
⋅
⋅
,
(
)
]
W
h
er
e
th
e
s
te
er
in
g
v
ec
to
r
a
(
)
,
n
=1
,
2
,
⋅
⋅
⋅
,
N,
co
r
r
esp
o
n
d
in
g
to
th
e
ℎ
in
cid
e
n
t
s
ig
n
al
is
d
e
f
in
ed
a
s
a
(
)
=
[
1
,
(
1
,
)
,
⋅
⋅
⋅
,
(
−
1
,
)
]
,
p
h
ase
co
m
p
o
n
en
t
(
,
)
is
d
ef
in
e
d
as
v
(
d
m
,
)
=
[
−
2
(
)
]
,
an
d
{·
}
T
d
en
o
tes
th
e
tr
a
n
s
p
o
s
e.
I
t
i
s
co
n
s
id
er
ed
th
at
th
e
s
ig
n
al
an
d
th
e
n
o
is
e
ar
e
u
n
co
r
r
elate
d
an
d
th
u
s
th
e
co
v
ar
ian
ce
m
at
r
ix
is
f
o
r
m
u
late
d
as d
ef
in
ed
in
(
2
)
.
R
x
=
E
{
x
(
t
)
x
H
(
t
)
}
=
A
dia
g
(
σ
1
2
,
σ
2
2
,
⋅
⋅
⋅
,
σ
N
2
)
A
H
+
σ
n
2
I
,
(
2
)
T
h
e
u
n
c
o
r
r
elatio
n
b
etwe
en
t
h
e
s
o
u
r
ce
a
n
d
th
e
n
o
is
e
is
d
en
o
ted
i
n
(
2
)
b
y
in
tr
o
d
u
cin
g
m
u
ltip
le
v
ar
ian
ce
s
σ
1
2
,
σ
2
2
….
,
σ
N
2
co
r
r
esp
o
n
d
in
g
to
N
s
o
u
r
ce
s
.
E
x
p
e
ctatio
n
E
{·
}
f
o
r
th
e
c
o
m
p
o
n
e
n
t
x
(
t)
x
H
(
t
)
d
ef
in
es
th
e
co
v
a
r
ian
ce
m
at
r
ix
.
T
h
e
id
en
tity
m
atr
ix
I
M
with
s
ize
M
x
M.
Vec
to
r
izin
g
t
h
e
(
2
)
,
c
r
ea
tes
th
e
v
ir
tu
al
a
r
r
ay
f
r
o
m
c
o
v
ar
ian
ce
m
atr
ix
.
T
h
e
v
ec
to
r
izatio
n
in
v
o
lv
es Kh
atr
i Ro
a
(
KR
)
p
r
o
d
u
ct
in
th
e
(
3
)
.
Y
⋅
=
⋅
ve
c
⋅
(
R
x
)
=
ve
c
(
AR
s
A
H
)
+
⋅
2
ve
c
(
I
)
=
⋅
(
A
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s
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tio
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d
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e
d
in
[
32
]
.
2
.3
.
Sp
a
rse
B
a
y
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n m
o
del
ing
Sp
ar
s
e
B
ay
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lear
n
in
g
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SB
L
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d
is
cu
s
s
ed
in
[
32
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is
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id
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ativ
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Du
e
to
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v
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r
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b
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co
r
p
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atin
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th
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o
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s
o
u
r
ce
v
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r
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ce
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I
t
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d
is
cu
s
s
ed
in
[
32
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th
at
if
th
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s
ig
n
als
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llo
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a
cir
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u
s
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tr
ib
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h
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6
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
1
,
Octo
b
er
2021
:
36
7
-
37
5
370
a
nd
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1
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ay
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r
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k
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ts
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2
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is
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a
n
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m
ativ
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d
is
tr
ib
u
tio
n
to
c
o
m
p
lete
th
e
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ay
esian
m
o
d
el:
(
2
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∝
1
,
2
>
0
(
11)
All
th
e
d
is
tr
ib
u
tio
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s
d
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o
r
d
if
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tain
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jo
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PDF
to
f
o
r
m
th
e
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ay
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o
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el
d
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in
ed
in
(
1
2
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.
(
,
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2
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̂
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(
,
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(
)
(
2
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(
1
2
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W
ith
th
e
d
ev
elo
p
ed
B
ay
esia
n
m
o
d
el
th
e
B
ay
esian
in
f
er
e
n
c
e
an
d
th
e
s
o
lu
tio
n
s
ar
e
o
b
tai
n
ed
to
esti
m
ate
th
e
DOA
o
f
th
e
g
iv
en
s
ig
n
al.
On
ce
th
e
B
ay
esian
m
o
d
el
is
r
ea
d
y
with
all
th
e
p
r
io
r
s
co
m
b
in
ed
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th
e
p
o
s
ter
io
r
h
as
to
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e
o
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tain
ed
i
n
o
r
d
er
to
in
f
er
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r
o
m
th
e
s
ig
n
al.
T
h
u
s
,
an
ex
p
e
ctatio
n
m
a
x
im
izatio
n
alg
o
r
ith
m
is
ad
o
p
ted
t
o
f
i
n
d
th
e
s
o
lu
tio
n
.
2
.4
.
Cu
m
ula
t
iv
e
ba
s
is
v
ec
t
o
r
T
h
e
NNSB
L
b
ased
DOA
esti
m
atio
n
alg
o
r
ith
m
u
s
es
th
e
m
atr
ix
th
at
ac
ts
as
th
e
o
v
er
co
m
p
lete
d
ictio
n
ar
y
.
T
h
is
o
v
er
c
o
m
p
lete
m
atr
ix
is
g
en
e
r
ated
u
s
in
g
u
s
u
ally
a
G
a
u
s
s
ian
b
asis
v
ec
to
r
.
T
h
is
b
asis
v
ec
to
r
is
ad
v
an
ce
d
in
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
t
o
m
a
k
e
it
a
c
u
m
u
lativ
e
b
asis
v
ec
to
r
im
p
lem
e
n
tatio
n
.
I
n
NNSB
L
DOA
esti
m
atio
n
d
u
r
in
g
a
to
s
ea
r
ch
i
n
g
a
o
r
to
g
en
er
ate
m
an
if
o
ld
m
atr
ix
p
r
o
ce
s
s
in
g
tim
e
to
u
s
in
g
m
u
lti
b
asis
v
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to
r
u
s
in
g
m
o
r
e
th
an
o
n
e
b
asis
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ec
to
r
u
s
in
g
is
a
cu
m
u
lativ
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asis
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ec
to
r
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n
g
cu
m
u
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ec
to
r
f
o
r
g
en
er
atin
g
m
an
if
o
l
d
m
atr
ix
i
n
cr
ea
s
es
th
e
AOA
esti
m
atio
n
ac
cu
r
ac
y
.
Ga
u
s
s
ian
b
asis
v
ec
to
r
u
s
ed
in
f
in
d
in
g
m
an
if
o
ld
m
at
r
ix
(
)
is
g
iv
en
b
y
:
=
1
∞
(
)
(
′
)
(
1
3
)
2.
5
.
H
y
brid
ba
s
is
v
ec
t
o
r
a
n
d o
ptim
iza
t
io
n
I
n
th
is
p
ap
er
,
a
h
y
b
r
id
b
asis
v
ec
to
r
m
ea
n
s
u
s
in
g
two
d
if
f
e
r
en
t
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asis
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ec
to
r
s,
o
n
e
is
G
au
s
s
ian
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o
th
er
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h
y
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o
lic
tan
g
en
t
b
asis
v
ec
to
r
is
u
s
ed
to
f
in
d
m
an
if
o
ld
m
atr
ix
.
T
h
e
d
o
t
p
r
o
d
u
ct
in
th
e
in
f
in
ite
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im
en
s
io
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al
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tr
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d
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tan
ce
b
etwe
en
p
o
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ts
in
th
e
d
ata
s
p
ac
e.
T
h
e
an
g
le
b
etwe
en
th
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b
asis
v
ec
to
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s
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s
m
al
l
in
th
e
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ec
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r
s
p
ac
e,
if
two
p
o
in
t
s
in
th
e
d
ata
s
p
ac
e
ar
e
n
ea
r
b
y
:
(
,
)
=
(
−
|
|
−
|
|
2
)
(
1
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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n
esian
J
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lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Hyb
r
id
b
a
s
is
ve
cto
r
b
a
s
ed
u
n
d
erd
etermin
ed
b
ea
mfo
r
min
g
a
l
g
o
r
ith
m
in
o
p
timiz
ed
…
(
K
r
u
p
a
P
r
a
s
a
d
K
.
R
.
)
371
Hy
p
er
b
o
lic
tan
g
e
n
t
b
asis
v
ec
to
r
s
o
we
th
eir
p
o
p
u
lar
ity
to
n
eu
r
al
n
etwo
r
k
s
,
wh
ich
,
tr
ad
iti
o
n
ally
u
s
e
th
e
h
y
p
er
b
o
lic
tan
g
en
t
ac
ti
v
at
io
n
f
u
n
ctio
n
ℎ
(
(
.
′
)
+
)
.
T
h
e
h
y
p
er
b
o
lic
tan
g
en
t
o
f
a
d
o
t
p
r
o
d
u
ct
with
f
ix
ed
lin
ea
r
s
ca
lin
g
p
r
o
v
i
d
es
a
b
asis
v
ec
to
r
b
ased
m
a
n
if
o
l
d
m
atr
ix
A.
Ad
ju
s
tin
g
p
ar
am
eter
,
eq
u
ilib
r
iu
m
co
n
s
tr
ain
t
in
ter
ce
p
t
c
o
n
s
tan
t
.
T
h
e
o
v
er
co
m
p
lete
b
asis
v
ec
t
o
r
th
at
is
u
s
ed
in
th
e
NNS
B
L
alg
o
r
ith
m
is
ch
an
g
ed
with
th
e
cu
m
u
lativ
e
a
n
d
th
e
h
y
b
r
i
d
b
asis
v
ec
to
r
a
n
d
th
e
p
er
f
o
r
m
an
ce
is
ch
ec
k
ed
a
n
d
co
m
p
a
r
ed
.
PS
O
is
th
e
b
io
-
in
s
p
ir
ed
alg
o
r
ith
m
th
at
is
d
e
v
elo
p
e
d
b
y
f
o
r
m
u
latin
g
th
e
b
eh
a
v
io
r
o
f
th
e
b
ir
d
s
s
ea
r
ch
in
g
its
p
r
ey
[
2
7
]
.
T
h
e
in
d
ep
en
d
e
n
t
v
a
r
iab
les
o
f
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
ar
e
ass
u
m
ed
as
th
e
b
ir
d
s
an
d
th
e
o
b
jectiv
e
f
u
n
ctio
n
is
an
alo
g
o
u
s
to
t
h
e
ac
t
o
f
th
e
b
ir
d
f
in
d
in
g
th
e
p
r
ey
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
in
t
h
is
im
p
lem
en
tatio
n
is
th
e
MSE
b
etwe
en
th
e
DOA
esti
m
ated
f
r
o
m
t
h
e
alg
o
r
ith
m
an
d
th
e
ac
tu
al
DOA
o
f
th
e
in
cid
en
t
s
ig
n
als.
PS
O
b
ased
r
ec
o
n
f
ig
u
r
atio
n
is
d
o
n
e
b
y
c
h
an
g
in
g
th
e
d
is
tan
ce
b
etwe
en
th
e
an
ten
n
as.
T
h
e
f
o
r
m
u
latio
n
d
ef
in
i
n
g
th
e
an
t
en
n
a
r
ec
o
n
f
ig
u
r
ed
DOA
esti
m
atio
n
u
s
in
g
M
u
ltib
asis
v
ec
to
r
is
d
ev
elo
p
e
d
.
Dis
tan
ce
b
etwe
en
th
e
an
ten
n
a
is
o
p
tim
is
ed
u
s
in
g
th
e
PS
O
alg
o
r
ith
m
,
co
n
s
id
er
in
g
th
e
MSE
a
s
th
e
m
in
im
izatio
n
p
ar
a
m
eter
.
T
h
e
MSE
is
d
ef
in
ed
b
y
ca
lcu
latin
g
th
e
d
if
f
er
en
ce
b
etwe
en
th
e
ac
tu
al
DOA
an
d
th
e
DOA
esti
m
ated
.
T
h
is
p
ap
er
in
tr
o
d
u
ce
s
m
o
r
e
s
to
ch
asti
c
n
at
u
r
e
in
th
is
f
o
r
m
u
latio
n
u
s
in
g
th
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
th
at
r
ec
o
n
f
ig
u
r
es
th
e
an
ten
n
a,
s
ea
r
ch
f
o
r
th
e
b
est
d
is
tan
ce
b
etwe
en
th
e
an
te
n
n
as.
T
h
e
PS
O
is
a
b
io
-
in
s
p
ir
ed
alg
o
r
ith
m
th
at
is
m
ath
em
atica
lly
m
o
d
els th
e
ac
tiv
ity
o
f
th
e
b
ir
d
f
lo
ck
th
at
tr
ie
s
to
f
in
d
th
e
f
o
o
d
in
th
e
s
wa
r
m
.
Ma
th
em
atica
lly
th
e
b
ir
d
in
th
e
r
ea
l
wo
r
ld
is
em
u
lated
as
a
p
ar
ticle
in
th
e
s
ea
r
ch
s
p
ac
e.
As
th
e
b
ir
d
g
ain
s
k
n
o
wled
g
e
f
r
o
m
th
e
n
ei
g
h
b
o
u
r
in
g
b
ir
d
an
d
b
y
its
o
wn
r
ef
er
en
ce
s
im
ilar
ly
ea
ch
p
a
r
ticle
g
en
er
ated
g
ain
s
th
e
k
n
o
wled
g
e
o
n
th
e
c
o
n
v
e
r
g
en
ce
.
T
h
e
b
ir
d
m
o
v
es
f
aster
with
h
ig
h
er
ac
ce
ler
atio
n
wh
ile
th
e
b
ir
d
is
f
ar
awa
y
f
r
o
m
th
e
f
o
o
d
.
Similar
ly
,
th
e
p
ar
ticle
m
o
v
es
f
aster
in
th
e
s
ea
r
ch
s
p
ac
e
an
d
with
h
ig
h
er
m
o
v
em
en
t
wh
en
it
is
f
ar
f
r
o
m
co
n
v
er
g
en
ce
.
Nea
r
t
h
e
co
n
v
er
g
e
n
ce
s
p
ac
e
th
e
p
a
r
ticle
m
o
v
es
s
lo
wer
an
d
with
less
er
m
o
v
em
en
t.
P
a
r
t
i
cl
e
s
i
n
P
SO
a
r
e
t
h
e
s
a
m
p
le
s
p
a
c
e
o
f
s
o
l
u
ti
o
n
s
.
T
h
e
s
e
s
o
lu
t
i
o
n
s
a
r
e
o
p
t
i
m
i
ze
d
u
s
i
n
g
t
h
e
o
b
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
s
,
w
h
i
c
h
i
s
p
r
i
m
a
r
i
l
y
t
h
e
c
a
u
s
e
o
f
t
h
e
f
o
r
m
u
l
a
t
i
o
n
.
I
n
t
h
i
s
p
a
p
e
r
th
e
o
b
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
i
s
t
h
e
m
ea
n
s
q
u
a
r
e
e
r
r
o
r
t
h
a
t
i
s
b
e
tw
e
e
n
t
h
e
a
ct
u
a
l
DO
A
a
n
g
l
e
a
n
d
t
h
e
e
s
t
i
m
a
te
d
a
n
g
l
e
.
T
h
e
o
b
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
is
g
i
v
e
n
b
y
(
1
5
)
.
=
1
∑
(
−
̂
)
2
=
1
(
1
5
)
Her
e
n
is
th
e
n
u
m
b
er
o
f
s
ig
n
a
ls
in
cid
en
t
o
n
th
e
an
ten
n
as.
is
th
e
ac
tu
al
a
n
g
le
o
f
ar
r
iv
al
f
o
r
th
e
ith
s
ig
n
al
an
d
⏞
is
th
e
est
im
ated
an
g
le
o
f
ar
r
iv
al.
Op
tim
izatio
n
c
o
n
v
er
g
es to
war
d
s
th
e
m
in
im
izati
o
n
o
f
th
is
m
ea
n
s
q
u
ar
e
er
r
o
r
.
W
ith
a
less
er
n
u
m
b
er
o
f
p
ar
a
m
eter
s
,
th
e
co
n
v
er
g
en
ce
is
p
o
s
s
ib
le
u
s
in
g
t
h
e
PS
O
alg
o
r
ith
m
.
T
h
e
d
is
tan
ce
b
etwe
en
th
e
a
n
ten
n
as
is
th
e
p
ar
am
eter
th
at
is
p
o
p
u
l
ate
d
an
d
s
ea
r
ch
e
d
.
Po
s
itio
n
o
f
th
e
PS
O
p
ar
ticles
v
ar
ies
f
r
o
m
o
n
e
p
o
s
itio
n
to
an
o
th
er
b
y
ad
d
i
n
g
th
e
v
elo
ci
ty
f
u
n
ctio
n
t
h
at
d
r
iv
es
th
e
p
ar
ticles
to
war
d
s
th
e
co
n
v
er
g
en
ce
.
Velo
city
f
u
n
ctio
n
is
ad
d
ed
with
th
e
p
ar
ticles
t
o
m
o
v
e
f
r
o
m
o
n
e
p
o
s
itio
n
to
an
o
th
er
an
d
ch
ec
k
f
o
r
co
n
v
er
g
en
ce
in
th
e
n
ew
p
o
s
itio
n
.
I
n
ea
ch
iter
atio
n
,
th
e
p
ar
ticles
wi
ll
m
o
v
e
to
war
d
s
th
e
co
n
v
er
g
en
ce
.
Usu
ally
,
2
0
to
3
0
p
ar
ticles
ar
e
g
en
er
ated
in
th
e
f
ir
s
t
iter
atio
n
an
d
in
ea
ch
iter
atio
n
,
th
e
m
o
v
em
en
t
will
cr
ea
te
th
e
s
am
e
n
u
m
b
er
o
f
p
ar
ticles.
T
h
is
i
ter
ativ
e
p
r
o
ce
s
s
co
n
v
er
g
es
to
war
d
s
th
e
MSE
m
in
im
i
za
tio
n
.
T
h
e
v
elo
cit
y
v
alu
es g
et
ad
d
ed
to
t
h
e
p
ar
ticl
e
p
o
s
itio
n
v
alu
es to
o
b
tain
t
h
e
n
ew
p
o
s
itio
n
s
o
f
th
e
p
ar
ticles.
T
h
ese
p
ar
ticles g
et
th
e
id
ea
a
b
o
u
t
th
e
n
eig
h
b
o
r
in
g
p
ar
ticles
an
d
th
e
p
a
r
ticles
th
at
ar
e
n
ea
r
to
t
h
e
co
n
v
er
g
en
ce
p
o
in
t
f
r
o
m
th
e
ve
lo
city
f
u
n
ctio
n
d
e
f
in
ed
i
n
(
1
6
)
.
1
←
1
+
1
1
(
−
1
)
+
2
2
(
−
1
)
(
1
6
)
ve
l
1
is
th
e
v
elo
city
f
u
n
ctio
n
wh
ic
h
is
f
o
r
m
ed
b
y
th
e
u
s
e
o
f
th
e
cu
r
r
en
t
p
o
s
itio
n
v
alu
es
1
,
,
co
n
s
tan
ts
1
,
2
an
d
1
,
2
,
Pb
est
is
th
e
b
est p
ar
ticle
in
th
e
p
r
ev
i
o
u
s
iter
atio
n
an
d
Gb
est
is
th
e
b
est
p
ar
ticle
f
o
r
al
l
th
e
iter
atio
n
s
ca
r
r
ied
o
u
t.
C
o
n
s
tan
ts
1
an
d
2
ar
e
ch
o
s
en
to
b
e
in
teg
er
2
.
W
h
il
e,
1
,
2
ar
e
r
an
d
o
m
ly
g
en
er
ate
d
:
1
←
1
+
1
(
1
7
)
T
h
e
ad
d
ed
s
u
m
o
f
th
e
p
r
ev
io
u
s
p
o
s
itio
n
v
alu
es
an
d
th
e
v
elo
city
v
alu
es
p
r
o
d
u
ce
s
th
e
n
ew
p
o
s
itio
n
v
alu
es
to
f
u
r
th
er
th
e
p
r
o
ce
s
s
o
f
s
ea
r
ch
[
2
9
]
.
‘
w’
is
th
e
in
er
tia
weig
h
t
co
n
tr
o
l
o
r
th
e
r
ate
at
wh
ich
th
e
v
elo
city
v
ar
ies
an
d
ch
o
s
en
b
etwe
en
0
.
4
to
0
.
9
.
T
h
e
p
s
eu
d
o
c
o
d
e
o
f
th
e
PS
O
o
p
ti
m
ized
alg
o
r
ith
m
is
as g
iv
en
in
th
e
f
o
llo
win
g
:
−
I
n
itialize
p
o
p
u
latio
n
(
Dis
tan
ce
o
f
an
ten
n
as a
r
e
p
o
p
u
lated
(
to
t
al
s
ix
v
ar
iab
le
is
p
o
p
u
lated
)
)
−
E
v
alu
ate
th
e
p
o
p
u
lated
p
ar
ticl
es (
i.e
d
is
tan
ce
f
o
r
th
e
lo
west M
SE
(
o
b
jectiv
e
f
u
n
ctio
n
)
)
−
Fin
d
th
e
b
est f
it v
alu
e
a
n
d
c
o
r
r
esp
o
n
d
in
g
s
et
o
f
d
is
tan
ce
s
(
Gb
est),
−
R
ep
ea
t
−
Fin
d
th
e
v
elo
city
v
alu
es f
r
o
m
(
1
6
)
−
Fin
d
n
ew
p
ar
ticles
by
(
1
7
)
−
Fin
d
th
e
b
est f
it v
alu
e
(
MSE
)
an
d
co
r
r
esp
o
n
d
in
g
s
et
o
f
d
is
tan
ce
s
(
Pb
est)
−
Up
d
ate
th
e
Gb
est v
alu
es Sto
p
wh
en
to
tal
n
u
m
b
er
o
f
iter
atio
n
s
is
co
m
p
leted
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
1
,
Octo
b
er
2021
:
36
7
-
37
5
372
I
n
th
is
p
ap
er
as
th
e
r
ec
o
n
f
ig
u
r
atio
n
is
d
ev
elo
p
ed
b
y
th
e
u
s
e
o
f
PS
O
th
e
d
is
tan
c
e
b
etwe
en
t
h
e
an
ten
n
a
ar
e
p
o
p
u
lated
in
o
r
d
er
to
g
et
th
e
o
p
tim
al
p
lace
m
en
t
o
f
th
e
an
ten
n
a
u
n
til
a
m
in
im
ized
MSE
o
cc
u
r
s
f
o
r
th
e
an
g
le
o
f
ar
r
i
v
al
esti
m
atio
n
.
T
h
e
DOA
esti
m
atio
n
is
im
p
r
o
v
ed
b
y
two
m
ea
n
s
in
th
is
im
p
l
em
en
tatio
n
.
On
e
is
th
e
cu
m
u
lativ
e/h
y
b
r
i
d
b
asi
s
v
ec
to
r
im
p
lem
e
n
tatio
n
in
th
e
c
r
ea
tio
n
in
th
e
m
an
i
f
o
ld
m
atr
ix
an
d
th
e
o
th
er
o
n
e
is
th
e
r
ec
o
n
f
ig
u
r
atio
n
o
f
th
e
an
te
n
n
a
p
o
s
itio
n
s
th
at
im
p
r
o
v
es th
e
DOA
esti
m
atio
n
s
till
f
u
r
th
e
r
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
A
n
o
v
el
a
n
ten
n
a
r
ec
o
n
f
ig
u
r
a
b
le
DOA
esti
m
atio
n
p
r
o
b
le
m
is
s
o
lv
ed
u
s
in
g
t
h
e
PS
O
alg
o
r
ith
m
.
A
cu
m
u
lativ
e
an
d
h
y
b
r
id
b
asi
s
v
ec
to
r
f
r
am
ewo
r
k
is
d
ev
elo
p
ed
o
n
th
e
NNSB
L
alg
o
r
ith
m
an
d
o
p
tim
ized
f
o
r
b
etter
DOA
esti
m
at
io
n
u
s
in
g
PS
O.
MA
T
L
AB
b
ased
s
im
u
latio
n
is
d
ev
elo
p
ed
f
o
r
th
e
PS
O
b
ased
r
ec
o
n
f
ig
u
r
atio
n
o
n
th
e
cu
m
u
lat
iv
e
b
asis
v
ec
to
r
b
ased
NNS
B
L
an
d
hybr
i
d
b
asis
v
ec
to
r
b
ased
NNSB
L
DOA
esti
m
atio
n
.
T
h
e
an
ten
n
a
s
ig
n
al
co
n
f
ig
u
r
atio
n
f
o
r
t
h
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
is
as sh
o
wn
in
th
e
T
ab
le
1
.
T
ab
le
1
.
An
ten
n
a
s
ig
n
al
co
n
f
i
g
u
r
atio
n
f
o
r
p
r
o
p
o
s
ed
DOA
esti
m
atio
n
al
g
o
r
ith
m
D
e
t
a
i
l
s
C
o
n
f
i
g
u
r
a
t
i
o
n
N
u
mb
e
r
o
f
A
n
t
e
n
n
a
s
6
A
n
t
e
n
n
a
A
r
r
a
y
t
y
p
e
N
o
n
-
u
n
i
f
o
r
m
A
n
g
l
e
R
a
n
g
e
-
π/
3
t
o
π
/
3
M
i
n
t
o
M
a
x
d
e
g
r
e
e
s
-
7
0
t
o
7
0
C
a
r
r
i
e
r
f
r
e
q
u
e
n
c
y
2
8
0
H
z
P
r
o
p
a
g
a
t
i
o
n
v
e
l
o
c
i
t
y
3
6
0
I
n
t
e
r
v
a
l
o
f
a
n
g
l
e
S
e
a
r
c
h
i
n
g
1
A
n
g
l
e
s
o
f
s
o
u
r
c
e
si
g
n
a
l
s
-
5
4
.
8
,
-
2
8
.
6
-
9
.
2
,
1
0
.
5
3
1
.
4
,
5
6
.
7
C
u
m
u
lativ
e
b
asis
v
ec
to
r
b
ase
d
m
an
if
o
ld
m
atr
ix
th
u
s
d
ev
el
o
p
ed
f
o
r
th
e
DOA
esti
m
atio
n
is
as
g
iv
en
in
th
e
Fig
u
r
e
1
.
As
p
er
th
e
T
a
b
le
1
th
er
e
ar
e
s
ix
s
ig
n
als
th
u
s
th
er
e
ar
e
s
ix
‘
A’
m
atr
ix
.
T
h
e
s
o
u
r
ce
s
ig
n
al
th
at
is
g
en
er
ated
w
h
ich
in
ci
d
en
ts
o
n
th
e
an
ten
n
a
is
as
d
ep
icted
in
Fig
u
r
e
2
.
T
h
e
s
ig
n
al
is
g
en
er
a
ted
with
th
e
ca
r
r
ier
f
r
eq
u
e
n
cy
o
f
2
8
0
Hz.
Fig
u
r
e
1
.
C
u
m
u
lativ
e
b
asis
v
ec
to
r
-
b
ased
m
a
n
if
o
ld
m
atr
ix
Fig
u
r
e
2
.
So
u
r
ce
s
ig
n
al
in
cid
e
n
t o
n
a
n
ten
n
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Hyb
r
id
b
a
s
is
ve
cto
r
b
a
s
ed
u
n
d
erd
etermin
ed
b
ea
mfo
r
min
g
a
l
g
o
r
ith
m
in
o
p
timiz
ed
…
(
K
r
u
p
a
P
r
a
s
a
d
K
.
R
.
)
373
T
h
e
n
o
is
e
is
ad
d
ed
with
th
e
s
o
u
r
ce
s
ig
n
al
wh
ich
is
th
e
wh
ite
ga
u
s
s
ian
n
o
is
e
an
d
th
e
DOA
esti
m
atio
n
p
r
o
ce
s
s
is
s
tar
ted
.
T
h
e
cu
m
u
lativ
e
b
asis
v
ec
to
r
b
ased
NNSB
L
(
C
B
VNN
SB
L
)
m
eth
o
d
is
co
m
p
ar
ed
with
h
y
b
r
id
b
asis
v
ec
to
r
b
ased
NNSB
L
(
H
B
VN
NS
B
L
)
an
d
f
o
u
n
d
th
at
th
e
MSE
is
co
m
p
etitiv
e
with
th
e
HB
V
NN
SB
L
m
eth
o
d
.
Fro
m
T
ab
le
2
it
ca
n
b
e
o
b
s
er
v
ed
th
at
th
e
MSE
is
r
ed
u
ce
d
in
th
e
H
B
VNNS
B
L
co
m
p
ar
ed
to
th
e
CBV
NNSB
L
m
eth
o
d
.
T
ab
le
2
.
An
ten
n
a
s
ig
n
al
co
n
f
i
g
u
r
atio
n
f
o
r
p
r
o
p
o
s
ed
DOA
esti
m
atio
n
al
g
o
r
ith
m
M
S
E
v
s SN
R
P
S
O
C
B
V
N
N
S
B
L
a
n
d
P
S
O
N
N
S
B
L
S
l
.
N
o
S
N
R
M
S
E
P
S
O
C
B
V
N
N
S
B
L
M
S
E
P
S
O
H
B
V
N
N
S
B
L
1
-
10
1
.
0
7
9
9
0
.
9
7
8
5
2
-
8
0
.
1
9
8
5
0
.
0
9
8
9
3
-
6
0
.
1
2
5
9
0
.
0
2
7
3
4
-
4
0
.
0
3
5
5
0
.
0
2
3
3
8
5
-
2
0
.
0
3
4
2
0
.
0
2
1
2
3
I
t
ca
n
b
e
o
b
s
er
v
e
d
f
r
o
m
th
e
T
ab
le
2
th
at
t
h
e
MSE
is
alm
o
s
t
ze
r
o
f
o
r
p
o
s
itiv
e
SNR
v
alu
es
wh
en
PS
O
C
B
V
NNS
B
L
an
d
PS
O
HB
V
NNSB
L
m
eth
o
d
s
ar
e
u
s
ed
.
T
h
e
r
ec
o
n
f
ig
u
r
atio
n
o
f
th
e
an
te
n
n
a
h
as
g
iv
en
b
etter
r
esu
lts
th
an
th
e
ad
v
a
n
ce
d
b
a
s
is
v
ec
to
r
m
eth
o
d
.
Fig
u
r
e
3
also
co
n
f
ir
m
s
th
at
th
e
HB
VNNSB
L
m
eth
o
d
is
co
m
p
etitiv
e
to
C
B
VNNS
B
L
m
eth
o
d
s
tak
en
f
o
r
d
is
cu
s
s
io
n
.
Fo
r
th
e
s
am
e
co
n
f
ig
u
r
atio
n
th
e
SNR
v
s
Sn
ap
s
h
o
ts
an
aly
s
is
is
ca
r
r
ied
o
u
t.
T
h
e
MSE
v
alu
es
o
b
tain
ed
f
o
r
v
a
r
iatio
n
in
th
e
s
n
ap
s
h
o
ts
ar
e
o
b
s
er
v
e
d
.
I
t
ca
n
b
e
o
b
s
er
v
ed
f
r
o
m
th
e
T
ab
le
3
th
at
th
e
MSE
is
alm
o
s
t
ze
r
o
f
o
r
s
n
ap
s
h
o
t
v
alu
es
wh
en
PS
O
-
HB
V
NNS
B
L
an
d
PS
OC
B
VNN
SB
L
m
eth
o
d
s
ar
e
co
m
p
ar
e
d
.
T
h
e
r
ec
o
n
f
ig
u
r
atio
n
o
f
t
h
e
an
ten
n
a
h
as
g
iv
en
b
etter
r
esu
lts
f
o
r
PS
O
HB
V
NNS
B
L
th
an
th
e
PS
O
C
B
VNN
SB
L
m
eth
o
d
.
Fig
u
r
e
4
c
o
n
f
ir
m
s
th
a
t th
e
PS
OHBV
NNS
B
L
m
eth
o
d
is
co
m
p
etitiv
e
PS
O
C
B
VN
N
SB
L
.
Fig
u
r
e
3
.
MSE
v
s
SNR
(
PS
O
C
B
V
NNS
B
L
,
PS
OH
B
VNN
SB
L
)
Fig
u
r
e
4
.
MSE
v
s
s
n
ap
s
h
o
ts
(
PS
O
-
C
B
V
NN
SB
L
,
PSO
H
B
V
NN
SB
L
)
T
ab
le
3
.
MSE
v
e
r
s
u
s
s
n
a
p
s
h
o
ts
(
PS
O
C
B
VNN
SB
L
v
s
P
SO
HB
VNN
SB
L
wi
th
v
ar
y
in
g
sn
ap
s
h
o
ts
)
M
S
E
v
s s
n
a
p
s
h
o
t
P
S
O
C
B
V
N
N
S
B
L
a
n
d
P
S
O
H
B
V
N
N
S
B
L
S
l
.
N
o
S
n
a
p
sh
o
t
M
S
E
P
S
O
C
B
V
N
N
S
B
L
M
S
E
P
S
O
H
B
V
N
N
S
B
L
1
5
0
1
.
0
3
8
0
0
.
9
3
7
6
2
1
0
0
0
.
7
2
9
8
0
.
6
2
6
9
3
1
5
0
0
.
3
6
8
0
0
.
2
6
8
9
4
2
0
0
0
.
1
8
5
0
0
.
0
8
7
6
5
2
5
0
0
.
1
5
8
6
0
.
0
7
8
7
4.
CO
NCLU
SI
O
N
Fro
m
th
e
r
esu
lts
an
d
d
is
c
u
s
s
io
n
,
th
e
o
b
jectiv
e
o
f
m
in
im
izatio
n
o
f
MSE
f
o
r
th
e
an
ten
n
a
r
ec
o
n
f
ig
u
r
atio
n
b
y
o
p
tim
izin
g
th
e
d
is
tan
ce
b
etwe
en
th
em
is
s
atis
f
ac
to
r
ily
p
er
f
o
r
m
ed
well.
T
h
e
co
m
p
etitiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
with
th
e
C
B
VNNSB
L
an
d
HB
V
NNS
B
L
alg
o
r
ith
m
is
ev
id
en
t f
r
o
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
1
,
Octo
b
er
2021
:
36
7
-
37
5
374
th
e
r
esu
lts
th
u
s
o
b
tain
ed
.
An
te
n
n
a
r
ec
o
n
f
ig
u
r
atio
n
u
s
in
g
PS
O
alg
o
r
ith
m
o
n
th
e
DOA
esti
m
atio
n
o
f
th
e
s
ig
n
als
o
n
a
n
o
n
-
u
n
if
o
r
m
lin
ea
r
ar
r
ay
is
d
ev
elo
p
ed
.
MSE
as th
e
o
p
tim
izatio
n
p
ar
am
eter
t
h
e
an
ten
n
a
r
ec
o
n
f
ig
u
r
atio
n
is
f
o
r
m
u
lated
as
a
Me
ta
h
e
u
r
is
tic
o
p
tim
izatio
n
p
r
o
b
lem
.
Dis
tan
ce
b
etwe
en
th
e
an
te
n
n
as
is
co
n
s
id
er
ed
as
th
e
in
d
ep
en
d
en
t
v
ar
iab
le
in
t
h
e
p
a
r
am
eter
o
p
tim
izatio
n
p
r
o
b
lem
th
u
s
d
ev
elo
p
ed
.
T
h
e
h
y
b
r
id
b
asis
f
u
n
ctio
n
b
ased
NNSB
L
m
eth
o
d
with
th
e
p
r
o
p
o
s
ed
an
te
n
n
a
r
ec
o
n
f
ig
u
r
ati
o
n
m
eth
o
d
with
PS
O
alg
o
r
ith
m
s
h
o
wed
b
etter
p
er
f
o
r
m
an
ce
in
t
h
e
DOA
esti
m
atio
n
m
eth
o
d
p
r
o
p
o
s
ed
.
T
h
e
r
esu
lts
ar
e
f
o
u
n
d
to
b
e
s
atis
f
ac
to
r
y
.
RE
F
E
R
E
NC
E
S
[1
]
R.
O.
S
c
h
m
id
t
,
“
M
u
lt
i
p
le
e
m
it
ter
lo
c
a
ti
o
n
a
n
d
sig
n
a
l
p
a
ra
m
e
ter
e
sti
m
a
ti
o
n
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
An
te
n
n
a
s
a
n
d
Pro
p
a
g
a
ti
o
n
,
v
o
l
.
3
4
,
n
o
.
3
,
p
p
.
2
7
6
-
2
8
0
,
1
9
8
6
,
d
o
i:
1
0
.
1
1
0
9
/
TAP
.
1
9
8
6
.
1
1
4
3
8
3
0
.
[2
]
R.
Ro
y
a
n
d
T.
Ka
il
a
th
,
“
Esp
rit
-
e
stim
a
ti
o
n
o
f
si
g
n
a
l
p
a
ra
m
e
ters
v
ia
ro
tati
o
n
a
l
i
n
v
a
rian
c
e
tec
h
n
iq
u
e
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Aco
u
stics
,
S
p
e
e
c
h
,
a
n
d
S
i
g
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
3
7
,
n
o
.
7
,
p
p
.
9
8
4
-
9
9
5
,
1
9
8
9
,
d
o
i:
1
0
.
1
1
0
9
/
2
9
.
3
2
2
7
6
.
[3
]
X
.
Yu
a
n
,
“
Dire
c
ti
o
n
-
fi
n
d
i
n
g
w
i
d
e
b
a
n
d
li
n
e
a
r
fm
so
u
rc
e
s
wit
h
tri
a
n
g
u
lar
a
rra
y
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Aer
o
sp
a
c
e
a
n
d
El
e
c
tro
n
ic S
y
ste
ms
,
v
o
l.
4
8
,
n
o
.
3
,
p
p
.
2
4
1
6
-
2
4
2
5
,
2
0
1
2
,
d
o
i:
1
0
.
1
1
0
9
/
TAES
.
2
0
1
2
.
6
2
3
7
6
0
0
.
[4
]
W
.
K.
M
a
,
T
.
H
.
Hs
ieh
,
a
n
d
C
.
Y
.
Ch
i,
“
DO
A
e
stim
a
ti
o
n
o
f
q
u
a
si
-
sta
ti
o
n
a
ry
si
g
n
a
ls
wit
h
les
s
se
n
so
rs
th
a
n
so
u
rc
e
s
a
n
d
u
n
k
n
o
wn
sp
a
ti
a
l
n
o
ise
c
o
v
a
ri
a
n
c
e
:
A
k
h
a
tri
-
ra
o
su
b
sp
a
c
e
a
p
p
r
o
a
c
h
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
5
8
,
n
o
.
4
,
p
p
.
2
1
6
8
-
2
1
8
0
,
2
0
1
0
,
d
o
i:
1
0
.
1
1
0
9
/T
S
P
.
2
0
0
9
.
2
0
3
4
9
3
5
.
[5
]
P
.
P
a
l
a
n
d
P
.
P
.
Va
id
y
a
n
a
th
a
n
,
“
Ne
ste
d
a
rra
y
s:
A
n
o
v
e
l
a
p
p
r
o
a
c
h
t
o
a
rra
y
p
ro
c
e
ss
in
g
wit
h
e
n
h
a
n
c
e
d
d
e
g
re
e
s
o
f
fre
e
d
o
m
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
ig
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l.
5
8
,
n
o
.
8
,
p
p
.
4
1
6
7
-
4
1
8
1
,
2
0
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
TS
P
.
2
0
1
0
.
2
0
4
9
2
6
4
.
[6
]
Z
.
Tan
,
Y.
C.
El
d
a
r,
a
n
d
A.
Ne
h
o
ra
i,
“
Dire
c
ti
o
n
o
f
a
rriv
a
l
e
stim
a
ti
o
n
u
si
n
g
c
o
-
p
rime
a
rra
y
s:
A
s
u
p
e
r
re
so
lu
t
io
n
v
iew
p
o
i
n
t,
”
I
EE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l.
6
2
,
n
o
.
2
1
,
p
p
.
5
5
6
5
-
5
5
7
6
,
2
0
1
4
,
d
o
i:
1
0
.
1
1
0
9
/
TS
P
.
2
0
1
4
.
2
3
5
4
3
1
6
.
[7
]
A.
M
o
ffe
t,
“
M
i
n
imu
m
-
re
d
u
n
d
a
n
c
y
li
n
e
a
r
a
rra
y
s
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
An
ten
n
a
s
a
n
d
Pro
p
a
g
a
ti
o
n
,
v
o
l.
1
6
,
n
o
.
2
,
p
p
.
1
7
2
-
1
7
5
,
1
9
6
8
,
d
o
i:
1
0
.
1
1
0
9
/
TAP
.
1
9
6
8
.
1
1
3
9
1
3
8
.
[8
]
H.
L.
Va
n
Tree
s,
“
Op
ti
m
u
m
a
rra
y
p
ro
c
e
ss
in
g
:
p
a
rt
IV
o
f
d
e
tec
ti
o
n
,
e
stim
a
ti
o
n
,
a
n
d
m
o
d
u
lati
o
n
,
”
In
J
o
h
n
W
il
e
y
&
Sons
,
Ne
w Yo
rk
,
USA,
2
0
0
2.
[On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s:/
/o
n
li
n
e
li
b
r
a
ry
.
wiley
.
c
o
m
/
d
o
i/
b
o
o
k
/1
0
.
1
0
0
2
/
0
4
7
1
2
2
1
1
0
4
[9
]
G
.
S
.
Blo
o
m
a
n
d
S
.
W.
G
o
lo
m
b
,
“
Ap
p
li
c
a
ti
o
n
s
o
f
n
u
m
b
e
re
d
u
n
d
i
re
c
ted
g
ra
p
h
s
,
”
Pro
c
.
IEE
E
,
v
o
l.
6
5
,
n
o
.
4
,
p
p
.
562
-
5
7
0
,
Ap
r.
1
9
7
7
,
d
o
i:
1
0
.
1
1
0
9
/P
ROC.1
9
7
7
.
1
0
5
1
7
.
[1
0
]
P.
P
a
l
a
n
d
P
.
P
.
V
a
i
d
y
a
n
a
t
h
a
n
,
“
Co
p
r
i
m
e
s
a
m
p
l
i
n
g
a
n
d
t
h
e
m
u
s
i
c
a
lg
o
r
i
t
h
m
,
”
i
n
2
0
1
1
D
i
g
i
t
a
l
S
i
g
n
a
l
P
r
o
c
e
ss
i
n
g
a
n
d
S
i
g
n
a
l
P
r
o
c
e
s
s
i
n
g
E
d
u
c
a
t
i
o
n
M
e
e
t
i
n
g
(
D
S
P
/
S
P
E
)
,
p
p
.
2
8
9
-
2
9
4
,
J
a
n
.
2011
,
d
o
i
:
1
0
.
1
1
0
9
/
D
S
P
-
S
P
E
.
2
0
1
1
.
5
7
3
9
2
2
7
.
[1
1
]
S
.
Q
i
n
,
Y
.
D
.
Z
h
a
n
g
,
a
n
d
M
.
G
.
Am
i
n
,
“
G
e
n
e
ra
l
i
z
e
d
c
o
p
r
i
m
e
a
r
ra
y
c
o
n
f
i
g
u
r
a
t
i
o
n
s
f
o
r
d
i
r
e
c
t
i
o
n
-
of
-
a
r
r
i
v
a
l
e
s
t
i
m
a
t
i
o
n
,
”
I
E
E
E
T
r
a
n
s
a
c
t
i
o
n
s
o
n
S
i
g
n
a
l
P
r
o
c
e
s
s
i
n
g
,
v
o
l
.
6
3
,
n
o
.
6
,
p
p
.
1
3
7
7
-
1
3
9
0
,
M
a
r
.
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
T
S
P
.
2
0
1
5
.
2
3
9
3
8
3
8
.
[1
2
]
S
.
A.
Ala
ws
h
a
n
d
A.
H.
M
u
q
a
i
b
e
l,
“
Th
re
e
-
lev
e
l
p
rime
a
rr
a
y
s fo
r
sp
a
rse
sa
m
p
li
n
g
in
d
irec
ti
o
n
o
f
a
rriv
a
l
e
stim
a
ti
o
n
,
”
I
n
2
0
1
6
IEE
E
Asia
-
P
a
c
if
ic
Co
n
fer
e
n
c
e
o
n
Ap
p
li
e
d
E
lec
tro
ma
g
n
e
ti
c
s
(AP
ACE
)
,
M
a
y
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/A
P
ACE.
2
0
1
6
.
7
9
1
6
4
4
1
.
[1
3
]
T.
Wa
n
g
,
B.
P
.
N
g
a
n
d
M
.
H.
E
r,
“
DO
A
e
stim
a
ti
o
n
o
f
a
m
p
l
it
u
d
e
m
o
d
u
late
d
s
ig
n
a
ls
wit
h
les
s
a
rra
y
se
n
s
o
rs
t
h
a
n
so
u
rc
e
s,”
I
n
2
0
1
2
IEE
E
I
n
ter
n
a
t
i
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Aco
u
stics
,
S
p
e
e
c
h
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
(ICA
S
S
P)
,
2
0
1
2
,
p
p
.
2
5
6
5
-
2
5
6
8
,
d
o
i:
1
0
.
1
1
0
9
/ICA
S
S
P
.
2
0
1
2
.
6
2
8
8
4
4
0
.
[1
4
]
X.
M
.
Ya
n
g
,
G
.
J.
Li
,
a
n
d
Z.
Zh
e
n
g
,
“
DO
A
e
stim
a
ti
o
n
o
f
n
o
n
c
ircu
lar
sig
n
a
l
b
a
se
d
o
n
s
p
a
rse
re
p
re
se
n
tatio
n
,
”
W
ire
les
s P
e
r
so
n
a
l
C
o
mm
u
n
ica
ti
o
n
s v
o
lu
me
,
v
o
l.
82
,
p
p
.
2
3
6
3
-
2
3
7
5
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
0
7
/s1
1
2
7
7
-
0
1
5
-
2
3
5
2
-
z
.
[1
5
]
P.
C
h
a
rg
e
,
Y.
Wan
g
,
a
n
d
J.
S
a
il
l
a
rd
,
“
A
n
o
n
-
c
ircu
lar
so
u
rc
e
s
d
ire
c
ti
o
n
fin
d
i
n
g
m
e
th
o
d
u
sin
g
p
o
ly
n
o
m
ial
ro
o
ti
n
g
,
”
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
81
,
n
o
.
8
,
p
p
.
1
7
6
5
-
1
7
7
0
,
2
0
0
1
,
d
o
i:
1
0
.
1
0
1
6
/S
0
1
6
5
-
1
6
8
4
(0
1
)0
0
0
7
1
-
8
.
[1
6
]
H.
Ab
e
id
a
,
a
n
d
J.
De
lma
s,
“
M
u
sic
-
li
k
e
e
stim
a
ti
o
n
o
f
d
irec
t
io
n
o
f
a
rriv
a
l
fo
r
n
o
n
c
ir
c
u
lar
so
u
rc
e
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l.
54
,
n
o
.
7
,
p
p
.
2
6
7
8
-
2
6
9
0
,
2
0
0
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7
]
M
.
Ha
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Ro
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r,
“
En
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it
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fo
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so
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s,”
In
2
0
0
4
IE
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In
ter
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C
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fer
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Ac
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Pr
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.
[1
8
]
J.
Li
u
,
Z
.
H
u
a
n
g
,
a
n
d
Y.
Z
h
o
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,
“
Ex
ten
d
e
d
2
q
-
m
u
sic
a
lg
o
rit
h
m
fo
r
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o
n
c
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lar
sig
n
a
ls,”
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i
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Pro
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g
,
v
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88
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1
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0
1
2
.
[1
9
]
H.
Ab
e
id
a
,
a
n
d
J.
De
lma
s,
“
S
tat
isti
c
a
l
p
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rfo
rm
a
n
c
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o
f
m
u
sic
-
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k
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a
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m
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i
n
re
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lv
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n
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n
c
ir
c
u
lar
so
u
rc
e
s,”
IEE
E
T
ra
n
sa
c
ti
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n
s
o
n
S
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g
n
a
l
Pr
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.
[
20]
F.
F
.
G
a
o
,
A.
Na
ll
a
n
a
t
h
a
n
,
a
n
d
Y
.
Wan
g
,
“
Im
p
ro
v
e
d
m
u
sic
u
n
d
e
r
th
e
c
o
e
x
isten
c
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o
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b
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t
h
c
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lar
a
n
d
n
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c
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la
r
so
u
rc
e
s,”
IEE
E
T
r
a
n
s.
S
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g
n
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l
Pr
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ss
.
,
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l.
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.
[2
1
]
Z.
T.
Hu
a
n
g
,
Z.
M
.
Li
u
,
J.
Li
u
,
a
n
d
Y.
Y.
Zh
o
u
,
“
P
e
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rm
a
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a
n
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ly
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o
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m
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c
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m
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u
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l
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g
,
”
I
ET
Ra
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S
o
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0
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.
[2
2
]
P
.
G
u
p
ta,
a
n
d
M
.
Ag
ra
wa
l,
“
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larity
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2
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8
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5
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.
[2
3
]
X.
Wan
g
,
a
n
d
X.
Li
n
,
“
C
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p
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m
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ro
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in
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wit
h
su
m
a
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d
d
iffere
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c
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-
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rra
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,
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In
2
0
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5
4
9
th
Asil
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m
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r
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2
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.
[2
4
]
S
h
o
Iwa
z
a
k
i
a
n
d
K
.
Ic
h
ig
e
,
“
S
u
m
a
n
d
d
iffere
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c
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p
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ig
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In
2
0
1
6
I
n
ter
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ti
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fe
re
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A
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v
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s
in
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375
[2
5
]
L
.
Li
u
,
J
.
Xu
,
Z
.
Hu
a
n
g
a
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d
G
.
Wan
g
,
“
Ad
jac
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c
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fo
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DO
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a
ti
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2
0
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7
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3
2
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.
[2
6
]
D.
Wi
p
f
,
a
n
d
B
.
Ra
o
,
“
An
e
m
p
iri
c
a
l
Ba
y
e
sia
n
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teg
y
f
o
r
so
lv
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n
g
th
e
sim
u
lt
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n
e
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s
s
p
a
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a
p
p
r
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x
ima
ti
o
n
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ro
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lem
,
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T
r
a
n
s
a
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.
[2
7
]
Z.
Zh
a
n
g
,
a
n
d
B.
Ra
o
,
“
S
p
a
rse
sig
n
a
l
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r
y
with
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p
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d
so
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to
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si
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g
sp
a
rse
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y
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sia
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rn
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g
,
”
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E
J
.
S
e
l.
T
o
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S
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ss
.
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l.
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.
2
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7
3
.
[2
8
]
J.
F
a
n
g
,
Y.
S
h
e
n
,
H.
Li
,
a
n
d
P
.
Wan
g
,
“
P
a
tt
e
r
n
-
c
o
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p
led
s
p
a
rse
Ba
y
e
sia
n
lea
rn
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g
fo
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v
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ry
o
f
b
l
o
c
k
-
sp
a
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sig
n
a
ls
,
”
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E
T
ra
n
s.
S
ig
n
a
l
Pro
c
e
ss
.
,
v
o
l.
63
,
n
o
.
2
,
p
p
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2
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3
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.
[2
9
]
J.
F
a
n
g
,
L.
Zh
a
n
g
,
a
n
d
H.
Li
,
“
Two
-
d
ime
n
sio
n
a
l
p
a
tt
e
rn
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c
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p
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,
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5
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5
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.
[3
0
]
Z.
Ya
n
g
,
L.
Xie
,
a
n
d
C.
Zh
a
n
g
,
“
Off
-
g
rid
d
irec
ti
o
n
o
f
a
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m
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ti
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sin
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sp
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in
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re
n
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e
,
”
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E
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ra
n
s.
S
i
g
n
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l
Pro
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ss
.
,
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l
.
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2
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2
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.
[3
1
]
Y.
Zh
a
n
g
,
Z
.
Ye
,
X.
Xu
,
a
n
d
N.
Hu
,
“
Off
-
gr
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d
DO
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stim
a
ti
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u
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v
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.
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6
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.
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0
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3
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1
1
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0
2
2
.
[3
2
]
N
.
Hu
,
B
.
S
u
n
,
J
.
Wan
g
,
J
.
Da
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a
n
d
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.
Ch
a
n
g
,
“
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rc
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lo
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lea
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,
”
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g
,
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ol
.
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2
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6
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B
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RAP
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y
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n
d
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strial
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e
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n
tati
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is
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m
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m
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IEE
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str
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tatio
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o
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d
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c
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str
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tatio
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.
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