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if
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w
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:
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:
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ed
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ar
t
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en
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I
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Hig
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k
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s
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m
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a
m
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ih
ed
r
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@
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co
m
1.
I
NT
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UCT
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O
N
S
m
ar
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tech
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An
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f
A
r
r
iv
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(
A
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)
e
s
ti
m
atio
n
a
lg
o
r
ith
m
s
,
w
h
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ar
e
d
iv
id
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in
to
t
w
o
d
if
f
er
en
t t
y
p
e
s
:
m
a
x
i
m
u
m
li
k
eli
h
o
o
d
DO
A
es
ti
m
atio
n
alg
o
r
it
h
m
a
n
d
s
u
b
s
p
ac
e
b
ased
DO
A
e
s
ti
m
atio
n
al
g
o
r
ith
m
.
T
h
e
m
a
x
i
m
u
m
li
k
eli
h
o
o
d
DO
A
esti
m
atio
n
al
g
o
r
ith
m
is
s
u
ch
as
M
L
esti
m
at
io
n
alg
o
r
ith
m
[
1
]
.
T
h
e
r
e
p
r
esen
tativ
e
s
u
b
s
p
ac
e
b
ased
DO
A
es
ti
m
atio
n
al
g
o
r
ith
m
s
a
r
e
MU
SIC
al
g
o
r
ith
m
[
2
]
,
E
S
P
R
I
T
alg
o
r
ith
m
[
3
]
,
an
d
W
S
F
al
g
o
r
ith
m
[
4
]
.
T
h
er
e
ar
e
v
ar
io
u
s
m
et
h
o
d
s
to
esti
m
ate
th
e
a
n
g
le
o
f
ar
r
iv
al
(
DO
A
)
o
f
r
ad
io
s
ig
n
a
ls
o
n
t
h
e
an
ten
n
a
ar
r
a
y
.
DO
A
esti
m
atio
n
tec
h
n
iq
u
e
s
ca
n
b
e
b
r
o
a
d
ly
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iv
id
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in
to
th
r
ee
d
if
f
er
e
n
t
ca
teg
o
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ies
n
a
m
el
y
;
co
n
v
e
n
tio
n
al
m
et
h
o
d
s
s
u
b
s
p
ac
e
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ased
m
et
h
o
d
s
an
d
m
a
x
i
m
u
m
l
ik
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ih
o
o
d
m
et
h
o
d
s
.
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o
n
v
o
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tio
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m
et
h
o
d
s
ar
e
b
ased
o
n
th
e
co
n
ce
p
ts
o
f
b
ea
m
f
o
r
m
in
g
an
d
n
u
ll
s
teer
i
n
g
,
b
u
t
it
r
eq
u
i
r
es
a
lar
g
e
n
u
m
b
e
r
o
f
elem
e
n
t
s
to
p
r
o
v
id
e
h
ig
h
r
eso
l
u
tio
n
.
E
x
a
m
p
les
o
f
t
h
is
m
eth
o
d
ar
e
d
ela
y
an
d
s
u
m
an
d
C
ap
o
n
‟
s
m
i
n
i
m
u
m
v
ar
ia
n
ce
m
et
h
o
d
[
5
]
.
On
e
m
aj
o
r
lim
ita
tio
n
o
f
th
is
m
eth
o
d
is
p
o
o
r
r
e
s
o
lu
tio
n
th
at
i
s
its
ab
ilit
y
to
s
ep
ar
ate
clo
s
ely
s
p
ac
ed
s
ig
n
als.
Un
l
ik
e
co
n
v
e
n
tio
n
al
m
eth
o
d
s
,
s
u
b
s
p
ac
e
m
e
th
o
d
s
ex
p
lo
it
th
e
in
f
o
r
m
a
tio
n
o
f
th
e
r
ec
eiv
ed
d
ata
r
esu
lt
in
g
in
h
i
g
h
r
eso
l
u
ti
o
n
.
T
w
o
m
ain
s
u
b
s
p
ac
e
b
ased
alg
o
r
it
h
m
s
ar
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Mu
l
tip
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S
ig
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C
la
s
s
i
f
icat
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d
E
s
ti
m
atio
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o
f
Si
g
n
al
P
ar
am
eter
s
v
ia
R
o
tatio
n
al
I
n
v
ar
ian
ce
T
ec
h
n
i
q
u
es [
6
]
.
T
h
e
DOA
alg
o
r
it
h
m
s
ar
e
class
i
f
ied
as
q
u
ad
r
atic
(
n
o
n
-
s
u
b
s
p
ac
e)
ty
p
e
an
d
s
u
b
s
p
ac
e
t
y
p
e.
T
h
e
B
ar
tlett
an
d
C
ap
o
n
(
Min
i
m
u
m
Var
ia
n
ce
Di
s
to
r
tio
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less
R
esp
o
n
s
e)
ar
e
q
u
ad
r
atic
t
y
p
e
a
lg
o
r
ith
m
s
.
B
o
th
m
et
h
o
d
s
ar
e
h
ig
h
l
y
d
ep
en
d
en
t
o
n
p
h
y
s
ical
s
ize
o
f
ar
r
a
y
a
p
er
tu
r
e,
w
h
ic
h
r
es
u
lt
s
i
n
p
o
o
r
r
eso
lu
tio
n
an
d
ac
cu
r
ac
y
.
S
u
b
s
p
ac
e
b
ased
DOA
e
s
ti
m
atio
n
m
et
h
o
d
is
b
as
ed
o
n
th
e
E
ig
e
n
d
ec
o
m
p
o
s
itio
n
.
T
h
e
s
u
b
s
p
ac
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Dir
ec
tio
n
o
f A
r
r
iva
l U
s
in
g
Un
ifo
r
m
C
i
r
cu
la
r
A
r
r
a
y…
(
Mo
h
a
mme
d
A
min
e
I
h
ed
r
a
n
e
)
31
b
ased
DOA
esti
m
atio
n
al
g
o
r
ith
m
s
MU
SIC
a
n
d
E
SP
R
I
T
p
r
o
v
id
e
h
i
g
h
r
eso
l
u
tio
n
;
th
e
y
ar
e
m
o
r
e
ac
cu
r
ate
an
d
n
o
t li
m
ited
to
p
h
y
s
ical
s
iz
e
o
f
ar
r
ay
ap
er
tu
r
e
[
7
-
8
]
.
T
h
ese
alg
o
r
ith
m
s
g
i
v
e
i
n
f
o
r
m
atio
n
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o
u
t
n
u
m
b
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o
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in
cid
e
n
t
s
i
g
n
al
s
an
d
DO
A
o
f
ea
ch
s
ig
n
al.
Ma
x
i
m
u
m
li
k
eli
h
o
o
d
m
eth
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d
is
o
n
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o
f
th
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f
ir
s
t
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n
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e
s
t
o
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e
in
v
esti
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ated
f
o
r
DO
A
est
i
m
atio
n
b
u
t
h
a
s
th
e
d
r
a
w
b
ac
k
o
f
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n
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e
n
s
i
v
e
co
m
p
u
tat
io
n
al
co
m
p
le
x
it
y
[
9
]
.
Dep
lo
y
ed
at
th
e
b
ase
s
tatio
n
o
f
th
e
ex
is
tin
g
w
ir
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s
s
i
n
f
r
a
s
tr
u
ct
u
r
e,
s
m
ar
t
an
ten
n
a
s
ar
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ca
p
ab
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o
f
b
r
in
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in
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o
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ts
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n
d
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p
ac
it
y
i
m
p
r
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v
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m
e
n
t
(
v
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r
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ar
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)
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en
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li
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ited
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a
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b
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m
ad
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n
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in
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ie
ld
o
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d
ig
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s
s
in
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,
w
h
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ab
le
s
m
ar
t
an
te
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n
as
to
d
y
n
a
m
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o
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o
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o
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e
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u
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er
.
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A
esti
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tec
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lo
g
y
i
s
f
o
cu
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ed
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g
h
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tio
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ti
m
atio
n
al
g
o
r
ith
m
.
I
n
m
u
lt
ip
le
DO
A
esti
m
ati
o
n
alg
o
r
ith
m
s
,
a
p
r
o
m
is
in
g
m
eth
o
d
f
o
r
s
m
ar
t
an
ten
n
a
ar
r
a
y
i
s
MU
SIC a
l
g
o
r
ith
m
[
1
0
]
.
I
t
h
as
b
ee
n
p
r
o
v
e
n
b
o
th
t
h
eo
r
etica
ll
y
a
n
d
e
x
p
er
i
m
e
n
tall
y
t
h
at
s
m
ar
t
an
te
n
n
as
ca
n
p
r
o
v
i
d
e
th
e
b
en
ef
it
s
s
ta
ted
ab
o
v
e,
b
u
t
p
o
s
s
ib
l
y
th
e
m
o
s
t
c
h
alle
n
g
in
g
p
r
o
b
lem
r
elate
d
to
ad
ap
tiv
e
an
ten
n
a
s
is
t
h
eir
p
r
ac
tical
im
p
le
m
e
n
tatio
n
[
6
]
.
Dig
ital
s
ig
n
al
p
r
o
ce
s
s
i
n
g
(
DS
P
)
alg
o
r
ith
m
s
r
elate
d
s
m
ar
t
a
n
ten
n
a
s
co
m
e
at
a
h
ig
h
co
m
p
u
tatio
n
al
ex
p
e
n
s
e
m
ak
in
g
t
h
eir
r
ea
l ti
m
e
i
m
p
le
m
en
tatio
n
d
i
f
f
icu
lt.
Un
til n
o
w
,
t
h
e
in
v
es
tig
a
tio
n
o
f
s
m
ar
t
a
n
te
n
n
as
s
u
itab
le
f
o
r
w
ir
ele
s
s
co
m
m
u
n
ica
tio
n
s
y
s
te
m
s
h
as
i
n
v
o
l
v
ed
p
r
i
m
ar
il
y
u
n
i
f
o
r
m
l
in
ea
r
ar
r
ay
s
(
UL
A
)
.
D
if
f
er
en
t
alg
o
r
it
h
m
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
th
e
esti
m
atio
n
o
f
t
h
e
d
ir
ec
tio
n
o
f
ar
r
iv
al
s
(
DOAs)
o
f
s
i
g
n
als
ar
r
iv
in
g
t
o
th
e
ar
r
ay
a
n
d
s
e
v
er
al
ad
ap
tiv
e
tec
h
n
iq
u
es
h
av
e
b
ee
n
ex
a
m
i
n
ed
f
o
r
th
e
s
h
ap
i
n
g
o
f
th
e
r
ad
iatio
n
p
atter
n
u
n
d
er
d
if
f
er
en
t
co
n
s
tr
ai
n
ts
i
m
p
o
s
ed
b
y
th
e
w
ir
eles
s
en
v
ir
o
n
m
e
n
t
[
1
1
]
,
[
1
2
]
.
A
lb
ag
o
r
y
e
t
al.
p
r
o
p
o
s
ed
an
ar
r
a
y
s
t
r
u
ctu
r
e
f
o
r
f
ast
an
d
co
m
p
u
ta
ti
o
n
all
y
ef
f
icie
n
t
2D
-
DO
A
est
i
m
a
tio
n
u
s
i
n
g
t
h
e
MU
SIC
alg
o
r
it
h
m
.
T
h
is
te
ch
n
iq
u
e
s
ep
ar
ates
t
h
e
n
o
is
e
an
d
th
e
s
i
g
n
al
s
u
b
s
p
ac
e
b
ased
o
n
ei
g
e
n
v
al
u
e
d
ec
o
m
p
o
s
itio
n
o
f
th
e
s
p
at
i
al
co
v
ar
ian
ce
m
atr
i
x
,
th
e
co
n
v
en
t
io
n
al
s
ig
n
al
p
r
o
ce
s
s
in
g
al
g
o
r
ith
m
s
[
1
3
]
.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
:
T
h
e
d
ef
i
n
itio
n
an
d
t
h
e
p
r
o
p
o
s
ed
Mu
s
ic
al
g
o
r
ith
m
ar
e
p
r
esen
ted
in
s
ec
tio
n
2
an
d
3
T
h
en
r
ea
lized
p
atch
an
ten
n
a
g
eo
m
etr
y
is
d
esi
g
n
ed
in
s
ec
tio
n
4
.
T
h
eir
s
i
m
u
lat
io
n
r
es
u
lt
s
a
n
d
t
h
e
c
o
m
p
ar
is
o
n
b
et
w
ee
n
t
h
e
MU
SIC
m
et
h
o
d
an
d
t
h
e
e
x
p
er
i
m
en
ted
o
n
es
ar
e
co
m
p
ar
ed
in
s
ec
tio
n
5
,
f
o
llo
wed
b
y
th
e
co
n
clu
s
io
n
.
2
.
M
O
DE
L
O
F
SI
G
NAL
S
AND
2
D
-
M
USI
C
AL
G
O
R
I
T
H
M
MU
SIC
m
ea
n
s
M
u
ltip
le
Si
g
n
al
C
las
s
i
f
icatio
n
[
1
4
]
,
is
a
h
ig
h
r
eso
lu
tio
n
DO
A
esti
m
atio
n
alg
o
r
ith
m
.
I
t
g
iv
e
s
t
h
e
es
ti
m
a
t
e
o
f
DO
A
o
f
s
i
g
n
als a
s
w
ell
as
th
e
es
ti
m
ate
o
f
th
e
n
u
m
b
er
o
f
s
ig
n
al
s
[
1
5
]
.
I
n
th
is
a
lg
o
r
it
h
m
,
t
h
e
es
ti
m
atio
n
o
f
DO
A
ca
n
b
e
ca
r
r
ied
o
u
t
b
y
u
s
in
g
o
n
e
o
f
th
e
s
u
b
s
p
ac
es
eith
er
n
o
is
e
o
r
s
ig
n
al.
T
h
e
ca
p
ac
it
y
o
f
D
O
A
esti
m
atio
n
u
s
in
g
U
C
A
-
MU
SI
C
s
h
o
w
s
i
n
Fig
u
r
e
1
is
b
o
u
n
d
e
d
b
y
t
h
e
n
u
m
b
er
o
f
an
ten
n
a
ele
m
en
t
s
.
T
h
ese
tech
n
iq
u
es
n
ee
d
to
esti
m
ate
th
e
DOAs
o
f
all
tar
g
et
s
ig
n
al
s
an
d
in
ter
f
er
e
n
ce
,
w
h
ic
h
d
ec
r
ea
s
es
t
h
e
ac
cu
r
ac
y
o
f
t
h
e
DO
A
es
ti
m
atio
n
[
1
6
-
1
7
]
.
W
e
ass
u
m
e
t
h
at
t
h
er
e
ar
e
N
u
n
i
f
o
r
m
cir
cu
lar
ar
r
a
y
,
M
n
ar
r
o
w
b
an
d
f
ar
f
ield
s
i
g
n
a
ls
f
r
o
m
d
i
f
f
er
e
n
t
in
cid
e
n
t
d
ir
ec
tio
n
.
T
h
e
r
ad
iu
s
o
f
t
h
e
cir
cu
lar
ar
r
ay
i
s
d
en
o
ted
as r
an
d
w
a
v
e
len
g
th
o
f
n
ar
r
o
w
b
a
n
d
is
d
en
o
t
ed
a
s
λ
.
Fig
u
r
e
1
.
Un
i
f
o
r
m
C
ir
c
u
lar
A
r
r
ay
(
U
C
A
)
w
it
h
N
ele
m
en
t
s
C
h
o
o
s
e
a
s
i
g
n
al
s
o
u
r
ce
S
(
t)
i
m
p
in
g
es
o
n
th
e
ar
r
a
y
w
it
h
a
n
an
g
le
θ.
I
f
t
h
e
r
ec
ei
v
ed
s
i
g
n
al
at
t
h
e
f
ir
s
t e
le
m
e
n
t is
x
1
(
t)
=
s
(
t)
,
th
en
th
e
d
ela
y
a
t e
le
m
e
n
t i
i
s
:
(
)
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
30
–
37
32
I
f
th
er
e
ar
e
M
s
o
u
r
ce
s
t
h
e
s
i
g
n
als r
ec
eiv
ed
at
th
e
ar
r
a
y
t
h
e
r
ec
eiv
ed
s
i
g
n
al
v
ec
to
r
is
g
iv
e
n
b
y
:
(
)
(
)
(
2
)
W
h
er
e
A
=[
a
(
)
,
…,
a
(
)
]
is
a
(
N×
M)
m
atr
ix
o
f
th
e
M
s
teer
i
n
g
v
ec
t
o
r
s
an
d
S=[
S1
(
t)
,
…,
SM(
t)
]
T
is
a
s
ig
n
al
s
o
u
r
ce
v
ec
to
r
o
f
o
r
d
er
(
M×
N)
.
d
en
o
te
tr
an
s
p
o
s
e
o
f
a
m
atr
i
x
an
d
N(
t)
is
N
(
t
)
=
[
n
1
(
t)
,
n
2
(
t)
,
.
.
.
,
n
N(
t)
]
T
is
th
e
tth
s
n
a
p
s
h
o
t
o
f
eit
h
er
ze
r
o
m
ea
n
s
tati
o
n
ar
y
co
m
p
lex
ad
d
iti
v
e
w
h
ite
g
au
s
s
ia
n
n
o
i
s
e
(
A
W
GN)
.
T
h
e
co
r
r
elatio
n
m
a
t
r
ix
o
f
r
ec
eiv
ed
v
ec
to
r
ca
n
b
e
w
r
itte
n
as:
(
3
)
W
h
er
e
is
th
e
v
ar
ian
ce
o
f
w
h
it
e
Gau
s
s
ia
n
n
o
is
e
v
ec
to
r
(
W
)
,
V
is
co
v
ar
ian
ce
m
a
tr
ix
o
f
s
i
g
n
al
v
ec
to
r
(
S)
w
h
ic
h
is
a
f
u
ll r
a
n
k
m
atr
i
x
o
f
o
r
d
er
(
M×
M)
g
iv
en
b
y
:
[
|
|
|
|
|
|
]
(
4
)
W
h
er
e
th
e
s
tati
s
tical
e
x
p
ec
tati
o
n
is
d
en
o
ted
b
y
E
[
]
,
R
s
is
a
s
ig
n
al
co
v
ar
ia
n
ce
m
a
tr
ix
o
f
o
r
d
er
(
N×
N)
w
it
h
r
an
k
M
g
iv
e
n
b
y
:
[
|
|
|
|
|
|
]
(
5
)
,
h
as
(
N
-
M)
E
ig
e
n
v
ec
to
r
s
c
o
r
r
esp
o
n
d
in
g
to
ze
r
o
eig
en
v
alu
e
s
.
W
e
k
n
o
w
t
h
at
s
teer
in
g
v
ec
to
r
(
)
w
h
ich
is
i
n
th
e
s
i
g
n
a
l
s
u
b
s
p
ac
e
is
o
r
th
o
g
o
n
al
to
n
o
is
e
s
u
b
s
p
ac
e
let
b
e
s
u
ch
an
eig
en
v
ec
to
r
.
(
6
)
Sin
ce
V
i
s
a
p
o
s
itiv
e
d
ef
in
i
te
m
atr
i
x
:
(
)
(
7
)
T
h
is
i
m
p
lie
s
t
h
at
s
ig
n
al
s
teer
in
g
v
ec
to
r
s
ar
e
o
r
t
h
o
g
o
n
al
to
eig
e
n
v
ec
to
r
co
r
r
esp
o
n
d
in
g
t
o
n
o
is
e
s
u
b
s
p
ac
e
[
9
-
1
1
]
.
So
th
e
MU
SI
C
alg
o
r
it
h
m
s
ea
r
ch
e
s
th
r
o
u
g
h
all
an
g
les a
n
d
p
lo
ts
th
e
s
p
atial
s
p
ec
tr
u
m
:
(
)
(
(
)
(
)
)
(
8
)
3
.
P
RO
P
O
SE
D
M
E
T
H
O
D
I
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
w
e
w
il
l r
ec
o
n
s
tr
u
ct
th
e
s
ig
n
al
m
at
r
ix
,
H=
E
X
*
(
9
)
W
h
er
e
„
*
‟
r
ep
r
esen
ts
co
m
p
le
x
co
n
j
u
g
ate,
E
is
an
N
o
r
d
er
in
v
er
s
e
id
en
tit
y
m
atr
ix
w
h
ic
h
is
ca
lled
tr
an
s
itio
n
m
atr
i
x
.
T
h
e
co
v
ar
ian
ce
m
atr
i
x
o
f
th
e
d
ata
H
is
:
W
e
in
tr
o
d
u
ce
a
n
e
w
ar
r
a
y
co
v
ar
ian
ce
m
atr
i
x
,
w
h
ich
i
s
th
e
s
u
m
o
f
R
Y
an
d
R
x
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Dir
ec
tio
n
o
f A
r
r
iva
l U
s
in
g
Un
ifo
r
m
C
i
r
cu
la
r
A
r
r
a
y…
(
Mo
h
a
mme
d
A
min
e
I
h
ed
r
a
n
e
)
33
A
cc
o
r
d
in
g
to
th
e
p
r
o
p
o
s
ed
m
atr
i
x
th
eo
r
y
,
if
q
i
s
an
e
ig
e
n
v
ec
to
r
co
r
r
esp
o
n
d
in
g
to
a
ze
r
o
eig
en
v
alu
e
o
f
m
a
tr
ix
A
R
S
A
,
t
h
en
q
m
u
s
t
also
b
e
a
n
e
ig
e
n
v
ec
to
r
co
r
r
esp
o
n
d
to
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i
f
o
r
m
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ir
c
u
lar
A
r
r
ay
(
U
C
A
)
w
it
h
5
ele
m
e
n
ts
Fig
u
r
e
8
.
(
4
x
4
)
p
lan
er
an
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n
a
w
it
h
0
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λ
T
o
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m
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ar
e
th
e
p
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o
p
o
s
ed
alg
o
r
ith
m
MU
SI
C
w
ith
a
n
ex
p
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i
m
e
n
ted
o
n
e
[
2
3
]
,
a
f
ir
s
t
s
i
m
u
latio
n
u
n
d
er
th
e
s
a
m
e
co
n
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itio
n
w
a
s
m
ad
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as
s
h
o
w
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i
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F
i
g
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e
9
w
e
e
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ed
t
w
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n
eq
u
a
l
p
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er
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als
ar
r
iv
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g
at
az
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m
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t
h
an
d
ele
v
a
tio
n
(
1
3
3
.
6
°,1
3
7
.
8
°)
an
d
(
7
8
.
6
°,
8
2
.
4
°),
r
esp
ec
tiv
ely
.
On
e
s
ig
n
al
p
o
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er
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s
7
d
B
m
a
n
d
t
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e
i
s
5
d
B
m
.
No
w
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e
d
ep
ict
t
h
e
s
p
ec
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u
m
o
f
th
e
MU
SI
C
a
n
d
t
h
e
th
i
s
w
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k
w
it
h
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eiv
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ata,
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e
n
o
te
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e
m
e
th
o
d
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s
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r
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e
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th
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atio
n
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2
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4
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1
3
6
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2
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d
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7
8
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8
4
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d
th
e
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k
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e
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ile
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e
M
u
s
ic
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3
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e
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ti
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atio
n
s
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3
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1
3
8
.
5
°)
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9
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8
2
.
5
°)
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e
f
ar
f
r
o
m
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1
3
3
.
6
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3
7
.
8
°)
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d
(
7
8
.
6
°,
8
2
.
4
°)
,
th
e
p
ea
k
s
ar
e
les
s
s
h
ar
p
.
Fig
u
r
e
9
.
Si
m
u
latio
n
f
o
r
az
i
m
u
th
a
n
d
elev
at
io
n
(
1
3
3
.
6
,
1
3
7
.
8
)
an
d
(
7
8
.
6
,
8
2
.
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
30
–
37
36
A
s
ec
o
n
d
s
i
m
u
latio
n
w
as
m
ad
e
b
y
c
h
an
g
i
n
g
th
e
az
i
m
u
th
an
d
ele
v
atio
n
,
(
1
2
8
.
4
°
,
1
1
6
°)
an
d
(
7
8
°,8
4
°),
r
esp
ec
tiv
ely
an
d
k
ee
p
in
g
s
i
g
n
als
p
o
w
er
i
n
7
d
B
m
a
n
d
5
d
B
m
.
I
t
i
s
o
b
s
er
v
ed
f
r
o
m
F
ig
u
r
e
1
0
ea
ch
al
g
o
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ith
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co
u
ld
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ep
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ate
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e
t
w
o
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g
n
al
s
,
w
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ile
co
m
p
ar
in
g
w
it
h
t
h
e
p
ea
k
o
f
t
h
e
MU
SI
C
alg
o
r
ith
m
[
2
4
]
,
an
d
t
h
e
p
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o
p
o
s
ed
alg
o
r
it
h
m
‟
s
i
s
m
u
c
h
s
h
ar
p
er
.
Fu
r
t
h
er
m
o
r
e,
t
h
e
DO
A
est
i
m
atio
n
s
o
f
t
h
e
n
e
w
m
et
h
o
d
ar
e
(
1
2
8
.
4
°,1
1
5
.
3
°)
an
d
(
7
8
°,8
4
°),
w
h
ich
is
m
o
r
e
ac
cu
r
ac
y
t
h
an
MU
SI
C
m
et
h
o
d
w
h
o
s
e
esti
m
atio
n
s
ar
e(
1
2
9
.
5
°,1
1
7
°)
a
n
d
(
7
8
.
5
°,8
2
.
5
°).
Fig
u
r
e
1
1
s
h
o
w
s
th
a
t
an
g
les
h
av
e
t
h
e
s
a
m
e
v
al
u
e
s
b
u
t
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e
d
ev
elo
p
ed
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et
h
o
d
p
r
esen
t
a
g
o
o
d
Ma
g
n
itu
d
e
f
o
r
an
g
le
s
(
9
9
.
4
8
5
0
.
1
3
)
;
(
6
4
.
8
8
1
5
.
1
)
th
e
m
ag
n
i
tu
d
e
is
4
0
.
1
5
d
B
,
3
8
.
8
4
d
B
r
esp
ec
tiv
el
y
.
R
es
u
lts
i
n
d
icate
t
h
at
t
h
is
w
o
r
k
g
iv
e
s
a
h
i
g
h
er
v
al
u
e
o
f
p
ea
k
s
an
d
esti
m
ates
s
u
cc
e
s
s
f
u
ll
y
t
h
e
an
g
le
s
w
it
h
a
n
ef
f
icien
t
m
ag
n
it
u
d
e:
+0
.
2
4
%
f
o
r
an
g
les
(
9
9
.
4
8
;
5
0
.
1
3
)
an
d
+0
.
0
2
%
f
o
r
an
g
les
(
6
4
.
8
8
1
5
.
1
)
co
m
p
ar
ed
to
th
e
p
r
o
p
o
s
ed
o
n
e
[
2
5
]
.
Fig
u
r
e
1
0
.
Si
m
u
latio
n
f
o
r
az
im
u
th
a
n
d
elev
atio
n
(
1
2
8
.
4
,
1
1
6
)
an
d
(
7
8
,
8
4
)
Fig
u
r
e
11.
Si
m
u
latio
n
f
o
r
az
im
u
th
a
n
d
elev
atio
n
(
9
9
.
4
8
,
5
0
.
1
3
)
an
d
(
6
4
.
8
8
,
1
5
.
1
)
W
e
co
n
clu
d
e
t
h
at
t
h
e
p
r
o
p
o
s
ed
UC
A
g
eo
m
etr
y
ca
n
o
b
tain
d
ir
ec
tl
y
t
h
e
m
ai
n
b
ea
m
to
w
a
r
d
s
th
e
u
s
er
an
d
at
t
h
e
s
a
m
e
ti
m
e
f
o
r
m
s
n
u
lls
i
n
th
e
d
ir
ec
tio
n
s
o
f
i
n
ter
f
er
er
s
i
n
t
h
e
ca
s
e
o
f
t
w
o
an
d
tr
ee
s
ig
n
als
an
d
w
e
ca
n
o
b
s
er
v
e
t
h
at
2
–
D
MU
SI
C
m
et
h
o
d
u
s
i
n
g
t
h
e
r
ec
o
n
f
i
g
u
r
ab
le
g
eo
m
etr
y
ca
n
b
e
ap
p
lied
f
o
r
co
r
r
elate
d
s
o
u
r
ce
s
to
eli
m
i
n
a
te
m
u
ltip
at
h
(
w
h
e
n
t
h
e
r
ec
o
n
f
i
g
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r
ab
le
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s
t
h
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d
esire
d
s
ig
n
al
a
n
d
its
v
ar
io
u
s
m
u
ltip
at
h
co
m
p
o
n
e
n
ts
)
.
6
.
CO
NCLU
SI
O
N
T
h
is
ar
ticle
p
r
esen
ts
t
h
e
r
esu
lts
o
f
d
ir
ec
tio
n
o
f
ar
r
iv
al
esti
m
atio
n
u
s
i
n
g
2
-
D
MU
SI
C
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
tak
e
s
f
u
ll
ad
v
an
ta
g
e
o
f
p
r
o
p
o
s
ed
g
eo
m
e
tr
ical
n
o
r
m
o
f
U
C
A
.
W
e
r
ec
o
m
m
en
d
M
USI
C
b
ased
o
n
UC
A
f
o
r
esti
m
atio
n
o
f
DO
A
s
y
s
te
m
f
o
r
th
e
f
o
llo
win
g
r
ea
s
o
n
s
:
i
t is
ea
s
ier
to
ar
r
a
n
g
e
th
e
cir
cu
lar
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Dir
ec
tio
n
o
f A
r
r
iva
l U
s
in
g
Un
ifo
r
m
C
i
r
cu
la
r
A
r
r
a
y…
(
Mo
h
a
mme
d
A
min
e
I
h
ed
r
a
n
e
)
37
ar
r
ay
o
n
a
n
air
cr
af
t
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r
a
s
atelli
te,
th
e
s
i
g
n
a
ls
w
it
h
t
h
e
s
a
m
e
f
r
eq
u
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c
y
ar
e
i
n
d
ep
en
d
en
t
f
o
r
ae
r
ial
r
ef
lecto
r
s
ar
o
u
n
d
t
h
e
a
n
te
n
n
a,
an
d
t
h
i
s
av
o
id
s
th
e
d
ef
ec
t
o
f
MU
SI
C
b
ased
o
n
U
C
A
,
t
h
e
h
i
g
h
SNR
o
f
a
ir
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o
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e
an
ten
n
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s
ca
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g
h
te
n
t
h
e
s
p
ec
tr
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m
p
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k
,
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n
cr
ea
s
e
p
r
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b
ab
ilit
y
o
f
s
ig
n
al
a
n
d
th
e
s
u
p
er
io
r
ity
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
co
m
p
ar
ed
to
th
e
ex
p
er
i
m
e
n
ted
o
n
es
w
i
th
a
m
ar
g
i
n
er
r
o
r
0
.
0
0
8
6
%.
RE
F
E
R
E
NC
E
S
[1
]
L
C
G
o
d
a
ra
.
A
p
p
li
c
a
ti
o
n
o
f
A
n
ten
n
a
A
rra
y
s
to
M
o
b
i
le
Co
m
m
u
n
ica
ti
o
n
s
P
a
rt
II
:
Be
a
m
-
f
o
r
m
in
g
a
n
d
Dire
c
ti
o
n
o
f
A
rriv
a
l
Co
n
sid
e
ra
ti
o
n
.
I
n
:
P
ro
c
e
e
d
in
g
s o
f
th
e
IEE
E.
1
9
9
7
,
p
p
.
1
1
9
5
-
2
4
5
.
[2
]
A
K
S
h
a
u
e
r
m
a
n
a
n
d
A
A
S
h
a
u
e
rm
a
n
.
S
p
e
c
tral
-
Ba
se
d
A
lg
o
rit
h
m
s
o
f
Dire
c
ti
o
n
-
of
-
A
rri
v
a
l
Esti
m
a
t
io
n
f
o
r
A
d
a
p
ti
v
e
Dig
it
a
l
A
n
ten
n
a
A
rra
y
s.
In
:
P
r
o
c
e
e
d
in
g
s
o
f
th
e
9
th
i
n
tern
a
ti
o
n
a
l
c
o
n
f
e
re
n
c
e
a
n
d
se
m
in
a
r
o
n
M
icro
/Na
n
o
tec
h
n
o
lo
g
ies
a
n
d
El
e
c
tro
n
De
v
ice
s,
No
v
o
sib
irsk
,
Ru
ss
i
a
.
2
0
1
0
,
p
p
.
2
5
1
-
5
5
,
[3
]
B
L
iao
a
n
d
S
C
Ch
a
n
.
DO
A
Esti
m
a
ti
o
n
o
f
Co
h
e
re
n
t
S
ig
n
a
ls
f
o
r
Un
if
o
rm
L
in
e
a
r
A
rra
y
s
w
it
h
M
u
tu
a
l
Co
u
p
li
n
g
.
In
:
P
r
o
c
e
e
d
in
g
s
o
f
th
e
I
EE
E
I
n
ter
n
a
ti
o
n
a
l
S
y
m
p
o
siu
m
o
n
Circu
it
s
a
n
d
S
y
ste
m
s,
Rio
d
e
Ja
n
e
ir
o
,
Bra
z
il
,
p
p
.
3
7
7
-
8
0
,
2
0
1
1
.
[4
]
M
Ja
lali,
M
N
M
o
g
h
a
d
d
a
si
a
n
d
A
Ha
b
ib
z
a
d
e
h
.
Co
m
p
a
rin
g
a
c
c
u
ra
c
y
f
o
r
M
L
,
M
USIC,
ROO
T
-
M
USIC
a
n
d
sp
a
ti
a
l
ly
s
m
o
o
th
e
d
a
lg
o
rit
h
m
s
f
o
r
2
u
se
rs.
In
:
P
r
o
c
e
e
d
in
g
s
o
f
th
e
IEE
E
c
o
n
f
e
re
n
c
e
M
e
d
it
e
rra
n
e
a
n
o
n
M
icro
w
a
v
e
S
y
m
p
o
siu
m
(M
M
S
),
T
a
n
g
iers
,
M
o
ro
c
c
o
,
p
p
.
1
-
5
,
2
0
0
9
[5
]
M
.
A
.
Ih
e
d
ra
n
e
,
S
.
Bri.
Dire
c
ti
o
n
o
f
a
rr
iva
l
i
n
two
d
i
me
n
sio
n
s
wit
h
m
a
trix
p
e
n
c
il
me
th
o
d
.
A
d
v
a
n
c
e
s in
In
telli
g
e
n
t
S
y
ste
m
s a
n
d
C
o
m
p
u
ti
n
g
.
6
4
0
.
p
p
.
2
1
9
-
2
2
8
.
[6
]
J.
Ca
p
o
n
,
R.
J.
G
re
e
n
f
ield
a
n
d
R.
J.
Ko
lk
e
r,
“
M
u
lt
i
d
im
e
n
sio
n
a
l
m
a
x
i
m
u
m
li
k
e
li
h
o
o
d
p
r
o
c
e
ss
in
g
o
f
a
larg
e
a
p
e
rtu
re
se
ism
ic arr
a
y
”
,
P
ro
c
e
e
d
in
g
s o
f
th
e
IEE
E,
1
9
6
7
,
5
5
(
2
),
p
p
1
9
2
-
2
1
1
.
[7
]
A
.
P
a
u
lraj,
R.
R
o
y
a
n
d
T
.
Ka
il
a
th
,
“
A
su
b
sp
a
c
e
ro
tatio
n
a
p
p
ro
a
c
h
t
o
sig
n
a
l
p
a
ra
m
e
ter
e
sti
m
a
ti
o
n
”
,
P
r
o
c
e
e
d
in
g
s o
f
th
e
IEE
E,
1
9
8
6
,
Vo
l7
4
(7
),
p
p
1
0
4
4
-
1
0
4
5
.
[8
]
L
C
G
o
d
a
ra
.
“
A
p
p
li
c
a
ti
o
n
o
f
A
n
ten
n
a
A
rra
y
s
to
M
o
b
il
e
C
o
m
m
u
n
ica
ti
o
n
s P
a
rt
II:
Be
a
m
-
f
o
r
m
in
g
a
n
d
Dire
c
ti
o
n
o
f
A
rriv
a
l
Co
n
sid
e
ra
ti
o
n
,
”
IEE
E
P
r
o
c
.
1
9
9
7
,
p
p
.
1
1
9
5
-
1
2
4
5
.
[9
]
C.
P
.
M
a
th
e
w
s,
M
.
D.
Z
o
lt
o
w
sk
i,
“
Ei
g
e
n
stru
c
t
u
re
tec
h
n
i
q
u
e
s
f
o
r
2
-
D
a
n
g
le
e
stim
a
ti
o
n
w
it
h
u
n
if
o
rm
c
ircu
lar
a
rra
y
s,” IE
E
E
T
ra
n
s o
n
S
P
,
v
o
l4
2
,
1
9
9
4
,
p
p
.
2
3
9
5
-
2
4
0
7
.
[1
0
]
Z.
A
h
m
a
d
,
I.
A
li
.
T
h
re
e
De
c
a
d
e
s
o
f
De
v
e
lo
p
m
e
n
t
in
DO
A
Esti
m
a
t
io
n
T
e
c
h
n
o
l
o
g
y
.
In
d
o
.
J.E
lel.
E
n
g
i
.
Co
m
p
.
S
c
ie
(IJEECS
).
V
o
l
1
2
(8
),
2
0
1
4
.
p
p
6
2
9
7
-
6
3
1
2
.
[1
1
]
M
.
A
.
Ih
e
d
ra
n
e
a
n
d
S
.
Bri,
“
2
-
D
DO
A
e
sti
m
a
ti
o
n
u
sin
g
M
USIC
a
lg
o
rit
h
m
w
it
h
u
n
if
o
rm
c
ircu
lar
a
rra
y
”
,
In
ter.
Co
ll
o
.
In
f
o
.
S
c
ie &
T
e
c
h
(CiS
t).
2
0
1
6
,
p
p
.
8
5
0
-
8
5
3
,
.
[1
2
]
El
.
A
.
B.
Ya
g
o
u
p
,
Z.
L
iu
a
n
d
Y.
X
u
,
DO
A
Esti
m
a
ti
o
n
b
y
F
o
u
rth
-
Ord
e
r
Cu
m
u
lan
ts
w
it
h
o
u
t
S
o
u
rc
e
En
u
m
e
ra
ti
o
n
a
n
d
Ei
g
e
n
d
e
c
o
m
p
o
siti
o
n
.
In
d
o
.
J.
El
e
l.
En
g
i.
C
o
m
p
.
S
c
ie
(IJEECS
).
2
0
1
4
,
1
2
(
6
),
p
p
.
4
2
3
7
-
4
2
4
2
.
[1
3
]
S
.
S
.
Ba
lab
a
d
ra
p
a
tr
u
n
i
,
P
e
rf
o
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
o
f
Dire
c
ti
o
n
o
f
A
rri
v
a
l
Esti
m
a
ti
o
n
Us
in
g
M
a
tl
a
b
,
S
ig
n
a
l
&
Im
a
g
e
P
ro
c
e
ss
in
g
.
In
t.
J; v
o
l
3
,
p
p
.
5
7
-
7
2
,
2
0
1
2
.
[1
4
]
R.
O.
S
c
h
im
d
.
“
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
s
.
A
n
ten
n
a
P
r
o
p
a
g
a
,
Vo
l
.
3
4
,
p
p
.
2
7
6
–
2
8
0
,
1
9
8
6
.
[1
5
]
P
a
n
a
y
io
ti
s
L
o
a
n
n
id
e
s,
a
n
d
C
o
n
st
a
n
ti
n
e
A
.
Ba
lan
is,
Un
if
o
rm
c
ircu
la
a
rra
y
s
f
o
r
s
m
a
rt
a
n
ten
n
a
s,”
IEE
E
A
n
ten
n
a
s
a
n
d
P
r
o
p
a
g
a
ti
o
n
M
a
g
a
z
in
e
,
v
o
l.
4
7
,
n
o
.
4
,
p
p
.
1
9
2
-
2
0
6
,
2
0
0
5
.
[1
6
]
M
a
ti
W
a
x
,
a
n
d
Ja
c
o
b
S
h
e
in
v
a
ld
,
“
Dire
c
t
io
n
f
in
d
i
n
g
o
f
c
o
h
e
re
n
t
sig
n
a
ls
v
ia
sp
a
ti
a
l
s
m
o
o
th
in
g
f
o
r
u
n
if
o
r
m
c
ircu
lar arra
y
s,” IE
EE
T
ra
n
s.
A
n
t
e
n
n
a
s an
d
P
ro
p
a
g
a
ti
o
n
,
v
o
l.
4
2
,
n
o
.
5
,
p
p
.
6
1
3
-
6
2
0
,
1
9
9
4
.
[1
7
]
M
.
A
.
Ih
e
d
ra
n
e
,
S
.
Bri
“
Dire
c
ti
o
n
o
f
A
rri
v
a
l
Esti
m
a
ti
o
n
u
sin
g
M
USIC,
ES
P
RIT
a
n
d
M
a
x
i
m
u
m
-
L
ik
e
li
h
o
o
d
A
l
g
o
rit
h
m
s f
o
r
A
n
ten
n
a
A
rra
y
s”
,
W
a
l.
J
.
S
c
.
T
e
c
(W
JST
),
V
o
l.
1
3
,
p
p
.
4
9
1
-
5
0
2
,
2
0
1
5
.
[1
8
]
T
.
A
I
-
M
a
z
n
a
e
e
a
n
d
H.
E.
A
b
d
-
EI
-
Ra
o
u
f
.
(2
0
0
9
).
De
sig
n
o
f
re
c
o
n
f
ig
u
ra
b
le
p
a
tch
a
n
te
n
n
a
w
it
h
a
sw
it
c
h
a
b
le
V
–
slo
t.
P
r
o
g
re
ss
in
g
In
El
e
c
tro
m
a
g
n
e
ti
c
s Re
se
a
rc
h
C,
6
,
1
4
5
-
1
5
8
.
[1
9
]
C.
W
Ju
n
g
,
K
im
,
K.
Yo
n
g
in
.
(2
0
0
8
).
Re
c
o
n
f
ig
u
ra
b
le
a
n
ten
n
a
fo
r
c
o
n
c
u
rre
n
t
o
p
e
ra
ti
o
n
o
v
e
r
c
e
ll
u
lar
a
n
d
c
o
n
n
e
c
ti
v
it
y
b
a
n
d
.
E
lec
tro
n
ic L
e
tt
e
rs,4
4
(5
)
,
3
3
4
-
3
3
5
,
[2
0
]
T
e
g
u
h
F
irm
a
n
s
y
a
h
ET
a
ll
.
Ba
n
d
w
id
th
a
n
d
G
a
in
E
n
h
a
n
c
e
m
e
n
t
o
f
M
IM
O
A
n
ten
n
a
b
y
Us
in
g
Rin
g
a
n
d
Circu
lar
P
a
ra
siti
c
w
it
h
A
ir
-
G
a
p
M
icro
strip
S
tru
c
t
u
re
.
T
EL
KO
M
NIK
A
(
Tele
c
o
m
m
u
n
ica
ti
o
n
,
C
o
m
p
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tr
o
l),
2
0
1
7
,
1
5
(3
),
p
p
.
1
1
5
5
-
1
1
6
3
[2
1
]
Y.
K.
Be
k
a
li
a
n
d
M
.
Essa
a
id
i.
(2
0
1
3
)Co
m
p
a
c
t
re
c
o
n
f
ig
u
ra
b
le
d
u
a
l
f
re
q
u
e
n
c
y
m
icro
strip
p
a
tch
a
n
t
e
n
n
a
f
o
r
3
G
a
n
d
4
G
m
o
b
il
e
c
o
m
m
u
n
ica
ti
o
n
te
c
h
n
o
l
o
g
ies
,
M
icro
w
a
v
e
a
n
d
Op
ti
c
a
l
T
e
c
h
n
o
lo
g
y
L
e
tt
e
rs,
5
5
(7
).
1
6
2
2
-
1
6
2
6
.
[2
2
]
M
.
A
.
Ih
e
d
ra
n
e
,
S
.
Bri
a
n
d
El
.
F
.
A
d
ib
a
.
Hig
h
Re
so
lu
ti
o
n
M
e
t
h
o
d
Us
in
g
P
a
tch
Circu
lar
A
rra
y
,
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
l
o
f
El
e
c
tri
c
a
l
a
n
d
C
o
m
p
u
ter E
n
g
in
e
e
rin
g
(IJECE).
V
o
l.
7
.
n
o
4
.
2
0
1
7
[2
3
]
W
.
J.
S
I
,
X.Y.
L
A
N
a
n
d
Y.
ZOU.
No
v
e
l
h
ig
h
-
re
so
lu
ti
o
n
DO
A
e
stim
a
ti
o
n
u
si
n
g
su
b
s
p
a
c
e
p
ro
jec
ti
o
n
m
e
th
o
d
,
J
.
Ch
i.
Un
i.
P
o
s.
T
e
l,
Vo
l.
1
9
,
p
p
.
1
1
0
-
1
6
,
2
0
1
2
[2
4
]
T
.
V
a
ru
m
,
J.
N.
M
a
to
s
a
n
d
P
.
P
i
n
h
o
.
Dire
c
ti
o
n
o
f
A
rri
v
a
l
Esti
m
a
ti
o
n
A
n
a
l
y
sis
Us
in
g
a
2
D
A
n
ten
n
a
A
rra
y
,
P
ro
c
e
.
T
e
c
h
n
,
V
o
l
1
7
,
p
p
.
6
6
7
-
6
2
4
,
2
0
1
3
.
[2
5
]
B.
S
u
n
.
M
USIC
Ba
se
d
o
n
Un
if
o
r
m
Cir
c
u
lar A
rr
a
y
a
n
d
Its
Dire
c
ti
o
n
F
in
d
i
n
g
Eff
icie
n
c
y
,
In
te.
J.
S
ig
n
.
P
ro
c
e
.
S
y
st,
V
o
l
1
,
p
p
.
2
7
3
-
2
7
7
,
2
0
1
3
.
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