I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
11
,
No
.
1
,
Feb
r
u
ar
y
2021
,
p
p
.
498
~
507
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
11
i
1
.
pp
4
9
8
-
507
498
J
o
ur
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l ho
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ep
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e
:
h
ttp
:
//ij
ec
e.
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m
Wireless
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liz
a
t
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o
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ed on
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ult
iple
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la
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ificatio
n alg
o
rith
m
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beel
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r
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rk
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(
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S
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s)
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re
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n
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m
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e
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n
t
th
e
n
se
n
d
s
in
f
o
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ti
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a
se
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ti
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t
o
tak
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p
p
ro
p
riate
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p
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ra
ti
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n
.
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W
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)
a
r
e
u
se
d
in
m
a
n
y
a
p
p
li
c
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ti
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h
trac
k
m
il
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a
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targ
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s,
d
isc
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ires
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stu
d
y
n
a
tu
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l
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su
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h
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rth
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a
k
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u
m
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y
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e
a
t,
e
tc.
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h
e
n
o
d
e
s
a
re
sp
re
a
d
in
larg
e
a
re
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s
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n
d
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i
c
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lt
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m
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ly
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m
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n
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e
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ti
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ro
m
s
e
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siti
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e
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les
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u
t
k
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o
w
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g
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h
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ir
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a
ti
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t
h
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a
se
a
p
ro
b
l
e
m
re
su
lt
e
d
in
th
e
p
o
sit
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n
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o
d
e
s.
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o
it
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n
a
c
c
e
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tab
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o
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e
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h
g
lo
b
a
l
p
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siti
o
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sy
ste
m
(G
P
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d
u
e
to
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s
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m
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ti
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th
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a
p
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e
x
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lain
e
d
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n
o
n
-
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P
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tec
h
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iq
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e
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lf
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in
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sin
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th
e
mu
lt
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le
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n
a
l
c
las
sif
ic
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ti
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(
M
USIC)
a
lg
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rit
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m
to
d
e
term
in
e
th
e
p
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siti
o
n
o
f
th
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c
ti
v
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se
n
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th
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stim
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ted
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irec
ti
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e
sig
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l
.
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h
e
n
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d
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ied
M
USIC
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lg
o
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h
m
(M
-
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USIC)
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e
th
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p
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lem
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h
e
re
n
t
sig
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l
.
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A
TL
AB
p
ro
g
ra
m
su
c
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ss
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u
ll
y
u
se
d
to
sim
u
late
th
e
p
ro
p
o
se
d
a
lg
o
rit
h
m
.
K
ey
w
o
r
d
s
:
DO
A
L
o
ca
lizatio
n
M
o
d
if
ied
alg
o
r
ith
m
MU
SIC a
l
g
o
r
ith
m
N
on
-
GP
S
W
SN
s
T
h
is i
s
a
n
o
p
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n
a
c
c
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ss
a
rticle
u
n
d
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r th
e
CC B
Y
-
SA
li
c
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n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Nab
ee
l A
ad
L
a
f
ta,
Dep
ar
t
m
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
B
ab
y
lo
n
U
n
i
v
er
s
it
y
,
I
r
aq
.
E
m
ail:
n
zu
r
f
y
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
W
ir
eless
s
e
n
s
o
r
n
et
w
o
r
k
s
co
n
s
is
t
o
f
s
m
all
s
en
s
o
r
n
o
d
es
t
h
at
s
p
r
ea
d
in
th
e
f
o
r
m
o
f
a
n
e
t
w
o
r
k
a
n
d
co
n
n
ec
t
w
ir
ele
s
s
l
y
w
it
h
a
b
as
e
s
tatio
n
t
h
at
p
er
f
o
r
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s
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h
e
f
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n
ctio
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en
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t
u
d
y
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p
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t
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at
m
a
y
o
cc
u
r
in
t
h
e
s
p
r
ea
d
in
g
e
n
v
ir
o
n
m
en
t
[
1
,
2
]
.
W
SNs
ar
e
th
e
r
esu
lt
o
f
m
u
ltip
le
s
ta
g
es
o
f
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ev
elo
p
m
e
n
t
d
u
r
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th
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p
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ad
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s
in
ce
1
9
5
0
-
1
9
9
0
a
p
p
ea
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e
d
th
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f
ir
s
t
g
lo
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te
m
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ir
eles
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e
n
s
o
r
s
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d
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esear
ch
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t
h
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s
f
ield
co
n
ti
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ed
to
d
ev
elo
p
,
in
2
0
0
1
I
n
tel
L
ab
s
an
n
o
u
n
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d
(
W
SNs
)
o
f
f
iciall
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.
I
t
u
s
ed
in
th
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m
ed
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d
m
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ld
s
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s
tu
d
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at
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ac
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a
tu
r
al
p
h
e
n
o
m
e
n
a
an
d
o
th
er
u
s
e
s
[
3
,
4
]
.
T
h
er
e
ar
e
m
an
y
ch
alle
n
g
e
s
an
d
p
r
o
b
lem
s
ar
e
th
e
s
e
n
s
o
r
n
et
w
o
r
k
s
s
u
f
f
er
s
f
r
o
m
i
t
s
u
ch
a
s
th
e
en
v
ir
o
n
m
e
n
t,
co
n
d
u
c
tio
n
an
d
p
o
w
er
p
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o
b
lem
s
,
an
d
s
to
r
ag
e
u
n
it
s
ize
,
b
u
t
t
h
e
m
o
s
t
i
m
p
o
r
tan
t
p
r
o
b
lem
is
th
e
lo
ca
tio
n
o
f
t
h
e
n
o
d
es [
5
]
.
Su
p
p
o
s
e
w
e
h
av
e
a
s
e
n
s
o
r
s
n
et
w
o
r
k
d
is
tr
ib
u
ted
in
a
lar
g
e
ar
ea
o
r
b
u
ild
in
g
s
u
ch
as
a
b
at
tlef
ield
o
r
f
o
r
est
c
h
ar
ac
ter
is
tic
p
u
r
p
o
s
e
f
o
r
th
ese
n
et
w
o
r
k
s
is
s
e
n
d
in
g
a
d
ata
to
a
b
ase
s
tat
io
n
i
n
a
d
e
f
i
n
ite
p
o
s
itio
n
[
6
,
7
]
.
B
u
t
th
i
s
n
o
d
es
is
a
n
o
n
y
m
o
u
s
l
o
ca
tio
n
th
u
s
g
en
er
ated
a
b
ig
p
r
o
b
lem
w
h
ic
h
is
lo
ca
ti
n
g
t
h
e
n
o
d
e
in
lar
g
e
W
SNs
w
h
e
r
e
t
h
e
d
a
t
a
s
e
n
t
f
r
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m
t
h
i
s
n
o
d
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b
a
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t
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a
t
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o
n
.
A
l
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s
[
8
,
9
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
Wir
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s
s
s
en
s
o
r
n
et
w
o
r
k’
s
lo
c
a
liz
a
tio
n
b
a
s
ed
o
n
mu
ltip
le
s
ig
n
a
l c
la
s
s
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tio
n
a
lg
o
r
ith
m
(
N
a
b
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l A
a
d
La
fta
)
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to
s
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lv
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th
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ca
tio
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m
u
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a
n
o
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GP
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W
S
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lo
ca
liza
tio
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a
n
d
s
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f
-
lo
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etec
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co
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ar
e
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a
n
u
al
s
etti
n
g
s
o
r
w
ith
GP
S
s
er
v
ice)
[
1
0
]
.
T
h
is
d
esig
n
i
s
k
n
o
w
n
as
t
h
e
ce
n
tr
al
n
o
d
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o
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Fi
g
u
r
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1
.
Fig
u
r
e
1
.
T
y
p
ical
W
SN a
r
ch
it
ec
tu
r
e
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
W
ir
eless
s
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d
tr
ac
k
in
g
s
y
s
te
m
s
w
it
h
t
h
e
d
e
v
elo
p
m
e
n
t
o
f
m
o
d
er
n
tech
n
o
lo
g
ie
s
w
h
ic
h
m
ad
e
a
lar
g
e
n
u
m
b
er
o
f
r
e
s
e
ar
ch
er
s
to
s
t
u
d
y
f
o
r
m
u
ltip
le
s
cien
tific
m
et
h
o
d
s
ai
m
i
n
g
f
o
r
t
h
e
s
a
m
e
p
u
r
p
o
s
e
wh
ich
is
to
f
i
n
d
th
e
lo
ca
tio
n
o
f
n
o
n
-
GP
S
w
ir
eles
s
s
en
s
o
r
s
in
(
W
SNs
)
as
f
o
llo
w
i
n
g
[
1
1
]
.
a.
T
h
e
m
eth
o
d
s
o
f
m
ea
s
u
r
in
g
t
h
e
d
is
tan
ce
b
et
w
ee
n
t
h
e
r
ef
er
e
n
c
e
n
o
d
e
an
d
th
e
s
e
n
s
o
r
n
o
d
e
s
u
ch
as
t
i
m
e
o
f
ar
r
iv
al
(
T
OA
)
tec
h
n
iq
u
e
w
h
ic
h
d
ep
en
d
s
o
n
m
ea
s
u
r
ed
th
e
d
is
tan
ce
b
y
ca
lcu
la
tin
g
th
e
ti
m
e
th
at
th
e
s
i
g
n
al
tak
es
w
h
e
n
s
e
n
d
in
g
it
f
r
o
m
th
e
s
e
n
s
o
r
n
o
d
e
u
n
t
il
it
'
s
ar
r
iv
ed
to
r
ef
er
en
ce
n
o
d
e
[
1
2
]
.
T
h
e
(
T
DOA
)
tech
n
iq
u
e
d
ep
en
d
s
o
n
m
ea
s
u
r
ed
th
e
d
if
f
er
en
ce
i
n
ti
m
e
o
f
a
r
r
iv
al
b
et
w
ee
n
t
w
o
s
ig
n
al
s
u
n
til
th
e
y
r
ea
ch
th
e
s
a
m
e
r
ef
er
en
ce
n
o
d
e
[
1
3
]
.
T
h
e
(
FDOA
)
tech
n
iq
u
e
d
ep
en
d
s
o
n
m
ea
s
u
r
ed
o
f
th
e
p
h
a
s
e
d
if
f
er
e
n
ce
b
et
w
ee
n
t
w
o
s
i
g
n
als
f
o
r
t
w
o
ele
m
en
t
s
o
f
th
e
a
n
te
n
n
a
[
1
4
]
.
A
n
d
th
e
(
R
SS
I
)
tech
n
iq
u
e
is
ca
lcu
lati
n
g
th
e
s
tr
en
g
t
h
o
f
t
h
e
i
n
co
m
in
g
s
i
g
n
al
b
y
co
m
p
ar
in
g
th
e
s
i
g
n
al
s
tr
en
g
t
h
a
n
d
t
h
e
e
x
p
ec
te
d
d
is
tan
ce
f
o
r
p
r
o
p
ag
atio
n
th
e
d
is
ta
n
ce
is
e
s
ti
m
ated
[
1
5
]
.
I
t
m
u
s
t
b
e
n
o
te
d
th
at
all
m
e
a
s
u
r
e
m
e
n
t
m
et
h
o
d
s
ab
o
v
e
lac
k
ac
cu
r
ac
y
a
n
d
th
eir
in
ab
il
it
y
to
lo
ca
te
m
u
l
tip
le
s
en
s
o
r
.
b.
An
o
th
er
lo
ca
tio
n
tec
h
n
iq
u
e
ca
lled
p
o
s
itio
n
ca
lc
u
latio
n
tech
n
iq
u
es
it
d
ep
en
d
o
n
th
e
m
at
h
e
m
atica
l
w
a
y
o
f
ca
lcu
lati
n
g
th
e
s
ite
b
y
r
el
y
in
g
o
n
th
r
ee
o
r
m
o
r
e
r
ef
er
en
ce
n
o
d
es
s
u
ch
a
s
(
T
ST)
T
r
ilater
at
io
n
s
ca
n
n
i
n
g
tech
n
iq
u
e
[
1
6
,
1
7
]
it
s
u
m
m
ar
i
ze
s
to
f
in
d
th
e
lo
ca
tio
n
o
f
a
s
en
s
it
iv
e
n
o
d
e
b
et
w
ee
n
t
h
r
ee
r
ef
er
en
ce
n
o
d
es
lo
ca
ted
at
th
e
in
ter
s
ec
t
io
n
p
o
in
t
o
f
t
h
e
t
h
r
ee
cir
cles
w
h
o
s
e
ce
n
tr
e
is
th
e
r
e
f
er
en
ce
n
o
d
es.
T
h
e
(
T
M)
tr
ian
g
u
lat
io
n
m
e
t
h
o
d
is
d
o
n
e
b
y
d
r
a
w
i
n
g
a
tr
ian
g
le
b
et
w
ee
n
t
w
o
r
e
f
er
en
ce
n
o
d
es
an
d
t
h
e
s
en
s
iti
v
e
n
o
d
e
an
d
u
s
e
m
at
h
e
m
atica
l
ca
lc
u
lat
io
n
to
f
i
n
d
t
h
e
lo
ca
tio
n
[
1
8
,
1
9
]
.
T
h
e
o
th
er
p
o
s
itio
n
i
n
g
m
et
h
o
d
is
(
P
ML
)
p
atter
n
m
atc
h
i
n
g
lo
ca
lizatio
n
it
is
d
ep
en
d
s
o
n
m
atc
h
i
n
g
t
h
e
r
ec
eiv
ed
s
i
g
n
al
w
it
h
t
h
e
o
th
er
r
ec
o
r
d
e
d
s
ig
n
al
as
a
r
ef
er
en
ce
[
2
0
,
2
1
]
.
T
h
is
m
eth
o
d
h
a
v
e
a
g
o
o
d
ac
c
u
r
ac
y
b
u
t
h
ig
h
ex
p
e
n
s
i
v
e
b
ec
au
s
e
it
n
ee
d
s
a
lar
g
e
n
u
m
b
er
o
f
r
e
f
er
en
ce
n
o
d
es a
n
d
th
eir
i
n
ab
ilit
y
to
lo
ca
te
m
u
ltip
le
s
e
n
s
o
r
.
c.
P
o
s
itio
n
in
g
alg
o
r
it
h
m
:
in
t
h
i
s
tech
n
iq
u
e,
t
h
e
d
is
tan
ce
a
n
d
th
e
p
o
s
itio
n
in
f
o
r
m
atio
n
ar
e
g
r
o
u
p
to
en
h
a
n
ce
m
en
t
an
d
m
ap
p
in
g
t
h
e
n
o
d
e
lo
ca
tio
n
ac
cu
r
atel
y
[
2
2
]
b
y
u
s
ed
MU
SI
C
al
g
o
r
ith
m
to
ca
lcu
late
o
r
esti
m
ate
th
e
a
n
g
les
o
f
t
h
e
s
i
g
n
al
s
r
ec
eiv
ed
f
r
o
m
th
e
s
en
s
itiv
e
s
e
n
s
o
r
s
t
h
at
ar
e
r
e
ce
iv
e
d
b
y
th
e
ar
r
a
y
an
ten
n
a
[
2
3
]
.
T
h
e
MU
SIC
alg
o
r
ith
m
i
s
b
ased
o
n
a
n
al
y
s
in
g
t
h
e
d
ata
m
atr
ix
a
n
d
t
h
en
ex
tr
ac
tin
g
th
e
ar
r
iv
i
n
g
a
n
g
les [
2
4
]
.
T
h
is
m
et
h
o
d
is
c
h
ar
ac
ter
ized
b
y
h
i
g
h
s
p
ee
d
in
d
ea
lin
g
w
i
th
m
u
lti
p
le
s
ig
n
al
s
an
d
ac
cu
r
ac
y
o
f
t
h
e
r
e
s
u
l
t
s
b
u
t
t
h
e
r
e
i
s
d
r
a
w
b
a
c
k
i
n
t
h
i
s
m
e
t
h
o
d
w
h
i
c
h
c
a
n
'
t
t
o
e
s
t
i
m
a
t
e
t
h
e
a
n
g
l
e
s
f
o
r
t
h
e
c
o
h
e
r
e
n
t
s
i
g
n
a
l
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
1
,
Feb
r
u
ar
y
2021
:
498
-
507
500
3.
P
RO
B
L
E
M
ST
AT
E
M
E
NT
I
n
th
i
s
p
ap
er
ass
u
m
e
th
at
t
h
er
e
is
n
o
p
r
io
r
k
n
o
w
led
g
e
a
b
o
u
t
th
e
p
o
s
itio
n
o
f
t
h
e
tar
g
et
n
o
d
es.
T
h
e
r
an
d
o
m
d
ep
lo
y
m
e
n
t
o
f
w
ir
ele
s
s
s
e
n
s
o
r
n
e
t
w
o
r
k
s
W
SN
p
r
o
d
u
ce
s
th
e
p
r
o
b
lem
o
f
lo
ca
tin
g
a
n
d
it
is
i
m
p
o
s
s
ib
le
to
ta
k
e
ad
v
an
ta
g
e
o
f
th
e
d
ata
o
f
t
h
ese
s
e
n
s
o
r
s
w
it
h
o
u
t
k
n
o
w
n
t
h
eir
lo
ca
tio
n
t
h
e
n
b
ec
au
s
e
o
f
th
e
p
r
o
b
lem
to
u
s
e
o
f
th
e
GP
S
s
y
s
te
m
f
o
r
ea
ch
s
e
n
s
o
r
,
w
h
ic
h
is
r
ep
r
esen
ted
b
y
(
co
s
t,
s
ize,
an
d
en
v
ir
o
n
m
en
tal
im
p
ac
t)
.
T
h
er
ef
o
r
e,
it
is
n
ec
e
s
s
ar
y
to
u
s
e
an
ap
p
r
o
p
r
iate
m
eth
o
d
to
f
in
d
n
o
d
es
lo
ca
tio
n
w
it
h
o
u
t
u
s
in
g
GP
S
tech
n
iq
u
e
[
9
,
1
0
].
4.
T
H
E
(
M
USI
C)
AL
G
O
RI
T
H
M
I
t
w
as
p
r
o
j
ec
ted
in
1
9
7
9
b
y
S
C
HM
E
DT
.
(
MU
SIC)
p
r
o
d
u
ce
d
a
n
e
w
ep
o
ch
o
f
s
p
atial
al
g
o
r
ith
m
s
f
o
r
d
ir
ec
tio
n
f
i
n
d
in
g
th
e
s
e
alg
o
r
i
th
m
s
t
h
at
h
a
v
e
ch
ar
ac
ter
ized
g
r
o
w
an
d
i
m
p
r
o
v
e
m
e
n
t
a
n
d
it
b
ec
am
e
a
k
e
y
alg
o
r
ith
m
f
o
r
th
e
s
p
atial
s
p
ec
tr
u
m
s
y
s
te
m
.
T
h
e
m
et
h
o
d
o
lo
g
y
o
f
its
s
u
m
m
ar
ized
in
th
e
f
o
llo
w
i
n
g
s
tep
s
[
2
5
]
.
4
.
1
.
Arr
a
y
s
ig
na
l f
lo
w
T
h
e
f
ir
s
t
s
tep
o
f
MU
SI
C
alg
o
r
ith
m
m
et
h
o
d
o
lo
g
y
i
s
t
h
e
s
i
g
n
al
f
lo
w
o
f
t
h
e
ar
r
a
y
an
ten
n
a.
Fig
u
r
e
2
s
h
o
w
s
th
e
u
n
i
f
o
r
m
l
in
ea
r
ar
r
a
y
(
U
L
A
)
w
it
h
s
en
s
o
r
s
n
u
m
b
e
r
N
allo
w
it
r
ec
ei
v
e
a
n
ar
r
o
w
b
an
d
Sk
(
t)
s
o
u
r
ce
s
ig
n
al
s
f
r
o
m
t
h
e
d
e
s
ir
ed
u
s
er
s
co
m
i
n
g
f
r
o
m
d
if
f
er
en
t
a
n
g
l
e.
T
h
e
ar
r
ay
also
r
ec
ei
v
es
K
n
ar
r
o
w
b
an
d
s
o
u
r
c
e
s
ig
n
al
s
ar
r
iv
in
g
at
d
ir
ec
tio
n
s
(
θ1
,
θ2
.
.
.
θK
)
,
[
2
6
,
2
7
]
.
Fig
u
r
e
2
.
Sig
n
al
f
lo
w
i
n
(
U
L
A)
L
et
's S
k
(
t)
is
t
h
e
s
o
u
r
ce
s
i
g
n
al
an
d
as
m
atr
i
x
is
S
=
[
S
1
(
t
)
,
S
2
(
t
)
,
…
,
S
K
(
t
)
]
T
(
1
)
T
h
en
as a
m
atr
i
x
.
A
ar
e
d
en
o
ted
to
th
e
s
teer
in
g
v
ec
to
r
o
f
an
t
en
n
a
ele
m
e
n
t [
2
8
]
.
=
[
₁
(
₁
)
₁
(
₂
)
⋯
₁
(
ₖ
)
₂
(
₁
)
⋱
⋮
⋮
⋱
⋮
ₙ
(
₁
)
⋯
⋯
ₙ
(
ₖ
)
]
(2
)
(
)
:
d
en
o
ted
to
t
h
e
ele
m
e
n
t o
f
ar
r
a
y
an
te
n
n
a
i
m
p
ac
ted
o
n
t
h
e
r
ec
eiv
ed
s
i
g
n
al
r
ep
r
esen
ted
as,
(
)
=
[
−
(
−
1
)
2
]
(3
)
w
h
er
e;
λ
:
is
th
e
w
a
v
ele
n
g
th
.
d
:
is
th
e
ar
r
a
y
ele
m
e
n
ts
s
p
r
ea
d
,
θ
:
is
th
e
a
n
g
le
o
f
ar
r
iv
i
n
g
s
i
g
n
al,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
Wir
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s
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en
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ed
o
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mu
ltip
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ig
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l c
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ifica
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ith
m
(
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l A
a
d
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fta
)
501
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s
p
atial
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ar
m
o
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ic
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n
(
t)
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m
atr
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H
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itia
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o
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ig
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l
E
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tin
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le
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ar
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al
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t
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en
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o
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tatio
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ig
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ec
o
m
p
o
s
itio
n
o
f
co
r
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elatio
n
m
a
tr
ix
o
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ar
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a
y
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ig
n
al
t
h
e
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X
m
atr
i
x
h
a
v
e
a
n
E
ig
en
v
al
u
es
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o
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ted
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d
escen
d
in
g
f
o
r
m
w
h
ic
h
is
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1
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2
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N
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,
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h
e
b
ig
g
er
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i
g
en
v
al
u
es
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o
r
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atc
h
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i
g
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al
an
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s
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er
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atc
h
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h
er
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e,
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ig
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ir
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s
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ec
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m
a
tr
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ir
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D
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h
e
s
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atial
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p
ec
tr
u
m
P
m
u
(
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ef
i
n
ed
as:
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mu
(
)
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1
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)
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h
e
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en
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i
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ato
r
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ec
a
m
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ze
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v
al
u
e
at
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ar
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iv
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alle
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d
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θ)
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k
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y
th
is
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r
o
ce
d
u
r
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th
e
a
n
g
le
o
f
a
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n
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y
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ak
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θ
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h
an
g
e
a
n
d
est
i
m
ates t
h
e
p
ea
k
[
3
2
,
3
3
]
.
T
h
e
f
lo
w
c
h
a
r
t o
f
MU
SIC a
l
g
o
r
ith
m
s
h
o
w
s
in
Fi
g
u
r
e
3
.
Fig
u
r
e
3
.
MU
SIC a
lg
o
r
it
h
m
f
l
o
w
c
h
ar
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
1
,
Feb
r
u
ar
y
2021
:
498
-
507
502
5.
M
O
DIFIE
D
M
USI
C
(
M
-
M
USI
C)
A
L
G
O
R
I
T
H
M
T
h
e
MU
SIC
al
g
o
r
ith
m
is
g
o
o
d
f
o
r
th
e
s
p
atial
s
p
ec
tr
u
m
e
s
ti
m
atio
n
o
f
in
co
h
er
en
t
s
i
g
n
als.
B
u
t
w
h
e
n
s
ig
n
al
s
o
u
r
ce
s
ar
e
co
h
er
en
t
i
t
f
ails
to
d
etec
t
t
h
e
m
m
ea
s
u
r
e
it
as
a
o
n
e
s
i
g
n
al.
T
h
is
is
a
d
r
a
w
b
ac
k
h
in
d
er
i
n
g
th
e
u
s
e
o
f
t
h
is
al
g
o
r
ith
m
in
i
m
p
o
r
ta
n
t
an
d
lar
g
e
n
et
w
o
r
k
s
.
th
en
t
h
e
in
d
ep
en
d
en
t
s
ig
n
al
s
o
u
r
ce
s
th
at
r
ec
eiv
ed
b
y
t
h
e
ar
r
a
y
w
il
l
d
ec
r
ea
s
e
w
h
ich
lead
to
th
e
ar
r
a
y
co
r
r
elatio
n
m
atr
i
x
r
an
k
r
ed
u
ce
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d
t
h
e
n
u
m
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f
lar
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er
eig
en
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le
s
s
th
a
n
t
h
e
in
co
m
i
n
g
s
ig
n
al
s
p
atia
l
s
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ec
tr
al
c
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r
v
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o
t
p
r
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ea
k
th
u
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n
n
o
t
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tain
th
e
c
o
r
r
ec
t sig
n
al
DO
A
e
s
ti
m
a
tio
n
[
3
4
].
Hen
ce
to
est
i
m
a
te
th
e
co
h
er
en
t
s
ig
n
al
DO
A
ac
cu
r
atel
y
m
u
s
t
b
e
r
e
m
o
v
e
t
h
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co
r
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n
b
et
w
ee
n
th
e
s
i
g
n
als.
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h
is
s
ec
tio
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p
r
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v
id
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th
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m
eth
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o
f
m
o
d
i
f
ied
MU
S
I
C
al
g
o
r
ith
m
an
d
i
m
p
r
o
v
ed
it
b
y
p
lan
n
ed
th
e
d
ata
m
a
tr
ix
w
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th
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n
j
u
g
at
e
r
ec
o
n
s
tr
u
ctio
n
o
f
MU
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al
g
o
r
ith
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as
i
n
t
h
e
f
o
llo
w
in
g
.
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r
m
u
la
te
a
m
atr
i
x
tr
an
s
f
o
r
m
atio
n
J
is
an
Nt
h
-
o
r
d
er
o
p
p
o
s
ed
m
atr
i
x
k
n
o
w
n
tr
a
n
s
itio
n
m
atr
i
x
.
=
[
0
0
⋯
1
0
0
1
0
⋮
⋮
⋯
⋮
1
0
⋯
0
]
(
1
0
)
T
h
e
m
o
d
if
ied
co
r
r
elatio
n
m
atr
ix
R
XX
ca
lc
u
lated
f
r
o
m
ad
d
ed
r
ec
o
n
s
tr
u
ct
io
n
m
atr
i
x
to
t
h
e
co
r
r
elatio
n
m
atr
i
x
as b
elo
w
[
3
5
]
.
R
XX
=
R
X
+
J
R
X
X
∗
J
(
1
1
)
T
h
e
m
a
tr
ices
R
X
a
n
d
R
XX
h
a
v
e
t
h
e
s
a
m
e
n
o
is
e
s
u
b
s
p
ac
e
ac
co
r
d
in
g
to
m
a
tr
ices
p
r
o
p
er
ties
Fig
u
r
e
4
.
S
h
o
w
s
t
h
e
f
lo
w
ch
ar
t
f
o
r
(
M
-
MU
SI
C
)
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o
r
ith
m
to
ca
r
r
y
o
u
t
th
e
d
ec
o
m
p
o
s
itio
n
ch
ar
ac
ter
is
tic
o
f
R
XX
an
d
o
b
tain
its
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ig
e
n
v
ec
to
r
an
d
E
i
g
en
v
al
u
e
d
e
n
o
ted
to
t
h
e
n
u
m
b
e
r
o
f
est
i
m
a
ted
s
i
g
n
a
l
s
o
u
r
ce
s
p
lit
t
h
e
n
o
is
e
v
ec
to
r
an
d
th
e
n
u
s
e
t
h
is
n
e
w
n
o
is
e
v
ec
to
r
to
cr
ea
te
s
p
atial
s
p
ec
tr
u
m
to
d
eter
m
i
n
ed
o
r
esti
m
atio
n
t
h
e
an
g
le
v
a
l
u
e
o
f
DO
A
b
y
p
ea
k
s
ea
r
c
h
in
g
[
3
5
,
3
6
].
Fig
u
r
e
4
.
M
-
MU
SI
C
alg
o
r
it
h
m
f
lo
w
c
h
ar
t
6.
M
AT
L
AB
SI
M
UL
AT
I
O
N
Desig
n
a
MA
T
L
A
B
s
i
m
u
latio
n
o
f
a
W
SN
s
y
s
te
m
co
n
s
i
s
t
o
f
th
r
ee
n
o
d
e
s
en
s
o
r
s
r
an
d
o
m
l
y
d
is
tr
ib
u
ted
at
an
g
le
θ=
(
1
0
o
,
2
5
o
,
45
o
)
r
esp
ec
tiv
el
y
w
h
ic
h
ar
e
w
ir
e
less
l
y
co
n
n
ec
ted
to
a
r
ef
er
en
ce
n
o
d
e
eq
u
ip
p
ed
w
it
h
a
n
(
UL
A
)
a
n
te
n
n
a
h
a
v
e
n
u
m
b
er
o
f
ele
m
en
t
N=
4
a
n
d
t
h
e
ele
m
en
t
s
ep
ar
ate
b
y
0
.
5
λ
a
n
d
S
NR
2
0
d
B
p
r
o
g
r
a
m
m
ed
w
it
h
t
h
e
(
MU
SI
C
)
o
r
(
M
-
M
USI
C
)
alg
o
r
it
h
m
to
esti
m
ati
o
n
o
f
DO
A
a
n
d
as
s
u
m
e
a
s
p
ec
if
ic
en
v
ir
o
n
m
e
n
t
th
e
r
esu
lted
as f
lo
w
i
n
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
Wir
ele
s
s
s
en
s
o
r
n
et
w
o
r
k’
s
lo
c
a
liz
a
tio
n
b
a
s
ed
o
n
mu
ltip
le
s
ig
n
a
l c
la
s
s
ifica
tio
n
a
lg
o
r
ith
m
(
N
a
b
ee
l A
a
d
La
fta
)
503
6
.
1
.
Used inco
here
nt
s
ig
na
ls
T
est
th
e
p
er
f
o
r
m
a
n
ce
o
f
M
U
SIC
an
d
M
-
MU
SIC
al
g
o
r
ith
m
w
it
h
I
n
d
ep
en
d
en
t
(
i
n
co
h
er
en
t
)
n
ar
r
o
w
s
ig
n
al
s
e
n
d
f
r
o
m
n
o
d
es
r
ec
eiv
ed
b
y
(
UL
A
)
a
n
te
n
n
a
at
r
ef
er
en
ce
n
o
d
e
Fig
u
r
e
5
.
Sh
o
w
s
t
h
e
s
i
m
u
latio
n
r
esu
l
ts
w
h
ic
h
n
o
te
d
t
h
at
t
h
e
p
ea
k
s
o
f
th
e
c
h
ar
t
s
y
m
b
o
lize
t
h
e
lo
ca
t
io
n
s
o
f
th
e
n
o
d
e
s
e
n
s
o
r
s
it
s
y
m
b
o
lize
th
e
DO
A
w
h
i
c
h
s
h
o
w
s
t
h
e
e
f
f
i
c
i
e
n
c
y
o
f
t
h
e
M
U
S
I
C
a
l
g
o
r
i
t
h
m
t
o
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n
t
s
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n
a
l
s
.
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h
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n
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h
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a
m
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d
a
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a
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d
(
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U
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)
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h
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d
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d
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m
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s
u
l
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a
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n
F
i
g
u
r
e
6
.
I
t
a
l
m
o
s
t
i
d
e
n
t
i
c
a
l
i
n
d
i
c
a
t
i
n
g
t
h
e
a
c
c
u
r
a
c
y
o
f
t
h
e
m
o
d
i
f
i
e
d
a
l
g
o
r
i
t
h
m
b
u
t
w
e
w
i
l
l
d
is
co
v
er
th
e
s
u
p
er
io
r
it
y
o
f
t
h
e
(
M
-
MU
SIC)
alg
o
r
ith
m
to
esti
m
atio
n
o
f
DO
A
g
i
v
es
an
i
m
p
r
o
v
e
m
e
n
t
in
t
h
e
d
ir
ec
tio
n
esti
m
ate
s
i
n
ce
th
e
w
id
t
h
o
f
t
h
e
esti
m
a
te
s
i
g
n
al
b
a
n
d
b
ec
o
m
e
s
n
ar
r
o
w
w
h
ich
g
i
v
es
m
o
r
e
ac
c
u
r
ac
y
i
n
r
ea
d
in
g
.
Fig
u
r
e
5
.
MU
SIC
w
it
h
in
co
h
e
r
en
t si
g
n
al
Fig
u
r
e
6
.
MU
SIC,
M
-
M
USI
C
w
it
h
i
n
co
h
er
e
n
t si
g
n
a
l
6
.
2
.
Used co
here
nt
s
ig
na
l
s
I
n
th
is
s
ec
tio
n
th
e
p
r
ev
io
u
s
e
x
p
er
i
m
e
n
t
w
a
s
r
ep
ea
ted
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ata
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r
ee
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en
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als
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f
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n
t
a
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s
F
i
g
u
r
e
7
.
S
h
o
w
s
t
h
e
f
a
i
l
u
r
e
o
f
t
h
e
M
U
S
I
C
a
l
g
o
r
i
t
h
m
i
n
d
e
t
e
c
t
i
o
n
t
h
e
t
h
r
e
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s
i
g
n
a
l
s
a
n
d
s
h
o
w
e
d
t
h
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r
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s
u
l
t
s
a
s
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o
n
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s
i
g
n
a
l
.
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h
i
l
e
t
h
e
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-
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U
S
I
C
a
l
g
o
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t
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m
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c
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d
e
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i
n
d
is
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v
er
in
g
th
e
th
r
ee
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ig
n
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d
id
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g
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r
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n
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le
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as i
n
Fi
g
u
r
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8
.
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o
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th
e
d
i
f
f
er
en
ce
b
et
w
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en
t
h
e
t
w
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et
h
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d
s
w
h
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n
d
etec
ted
th
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co
h
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en
t
s
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g
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al
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th
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t
w
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p
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ts
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v
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w
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h
a
t
th
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USI
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alg
o
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it
h
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is
ef
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icie
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t
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e
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ti
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ati
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th
e
an
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f
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g
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co
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er
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n
t.
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u
t
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f
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e
s
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g
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ar
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er
en
t
th
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SI
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al
g
o
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ith
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n
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tin
g
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is
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et
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t
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ig
n
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an
d
d
ea
l
s
w
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t
h
t
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m
a
s
a
s
i
n
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s
i
g
n
a
l
.
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h
i
l
e
t
h
e
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-
M
U
S
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C
a
l
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o
r
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m
a
b
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d
e
t
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c
t
t
h
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s
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g
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a
l
s
i
n
b
o
t
h
c
a
s
e
s
w
i
t
h
h
i
g
h
a
c
c
u
r
a
c
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
1
,
Feb
r
u
ar
y
2021
:
498
-
507
504
Fig
u
r
e
7
.
MU
SIC
w
it
h
co
h
er
e
n
t si
g
n
al
Fig
u
r
e
8
.
MU
SIC,
M
-
M
USI
C
w
it
h
co
h
er
e
n
t si
g
n
al
7.
F
ACTOR
S A
F
F
E
CT
E
D
O
N
T
H
E
RE
SU
L
T
S
Fo
r
d
esig
n
p
o
s
it
io
n
i
n
g
s
y
s
te
m
b
y
t
h
i
s
alg
o
r
it
h
m
w
it
h
h
ig
h
q
u
alit
y
it
m
u
s
t
b
e
p
o
in
ted
o
u
t
th
at
t
h
er
e
ar
e
s
ev
er
al
f
ac
to
r
s
t
h
at
a
f
f
e
ct
th
e
r
es
u
lt
ac
cu
r
ac
y
o
f
es
t
i
m
ated
th
e
DO
A
w
h
en
u
s
ed
b
o
th
(
MU
SIC
o
r
M
-
MU
SI
C
)
alg
o
r
it
h
m
to
lo
ca
l
izatio
n
(
W
SNs
)
as t
h
e
f
o
llo
w
i
n
g.
7
.
1
.
N
u
m
ber
o
f
ele
m
ent
s
T
o
clar
if
y
th
e
DO
A
e
s
ti
m
atio
n
a
f
f
ec
ted
b
y
th
e
n
u
m
b
er
o
f
ar
r
ay
ele
m
e
n
ts
w
e
w
ill
r
ep
ea
t
th
e
p
r
ev
io
u
s
s
i
m
u
lat
io
n
s
i
n
s
ec
tio
n
(
6
)
w
i
th
t
h
e
s
a
m
e
d
ata,
a
n
d
a
ch
a
n
g
e
t
h
e
n
u
m
b
er
o
f
ele
m
e
n
ts
to
(
4
,
1
0
,
an
d
4
0
)
th
e
r
es
u
lts
in
t
h
e
f
o
llo
w
in
g
F
i
g
u
r
e
9
.
I
t is e
v
id
en
t
t
h
at
t
h
e
i
n
cr
ea
s
e
in
t
h
e
n
u
m
b
er
o
f
an
ten
n
a
ele
m
en
t
s
g
iv
e
s
an
i
m
p
r
o
v
e
m
en
t
i
n
t
h
e
d
ir
ec
tio
n
es
ti
m
ate,
s
i
n
ce
th
e
w
id
th
o
f
t
h
e
e
s
ti
m
ate
s
i
g
n
al
b
an
d
b
ec
o
m
e
s
n
ar
r
o
w
,
w
h
ic
h
g
i
v
es
m
o
r
e
ac
cu
r
ac
y
i
n
r
ea
d
in
g
,
an
d
it
m
u
s
t
b
e
n
o
ted
th
at
th
e
i
n
c
r
ea
s
e
in
t
h
e
ele
m
e
n
t
s
is
d
eter
m
in
ed
b
y
co
s
t a
n
d
co
m
p
lex
i
t
y
f
ac
to
r
s
an
d
m
u
s
t b
e
co
m
p
atib
le
w
i
th
t
h
e
n
atu
r
e
a
n
d
q
u
alit
y
th
e
w
o
r
k
.
7
.
2
.
Arr
a
y
ele
m
ent
s
pa
cing
T
o
clar
if
y
th
e
DO
A
e
s
ti
m
atio
n
a
f
f
ec
ted
b
y
th
e
s
p
ac
i
n
g
o
f
ar
r
ay
ele
m
e
n
ts
w
e
w
ill
r
ep
ea
t
th
e
p
r
ev
io
u
s
s
i
m
u
l
at
io
n
s
i
n
s
ec
tio
n
(
6
)
w
it
h
th
e
s
a
m
e
d
ata,
a
n
d
a
c
h
a
n
g
e
t
h
e
s
p
ac
i
n
g
o
f
ele
m
en
t
(
0
.
2
5
λ
,
0
.
5
λ
,
λ
)
Fi
g
u
r
e
1
0
.
Sh
o
w
t
h
e
r
esu
lts
t
h
at
i
n
cr
ea
s
i
n
g
t
h
e
d
is
ta
n
ce
b
et
w
ee
n
th
e
e
le
m
e
n
ts
o
f
th
e
a
n
te
n
n
a
u
p
to
h
al
f
th
e
w
a
v
ele
n
g
t
h
in
cr
ea
s
es
t
h
e
e
f
f
icien
c
y
o
f
e
s
ti
m
ati
n
g
t
h
e
d
ir
ec
tio
n
o
f
t
h
e
s
i
g
n
al.
B
u
t
w
h
en
th
e
d
is
ta
n
ce
b
e
co
m
e
s
g
r
ea
ter
t
h
an
h
al
f
th
e
w
a
v
ele
n
g
th
,
t
h
er
e
i
s
an
er
r
o
r
in
esti
m
atio
n
o
f
DO
A
,
an
d
in
o
r
d
er
to
o
b
tai
n
th
e
b
est
r
es
u
lt
s
,
th
e
d
is
ta
n
ce
b
et
w
ee
n
t
h
e
ele
m
en
ts
m
u
s
t b
e
k
ep
t c
lo
s
e
to
h
al
f
th
e
w
av
ele
n
g
t
h
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
Wir
ele
s
s
s
en
s
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r
n
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w
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k’
s
lo
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a
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n
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a
s
ed
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n
mu
ltip
le
s
ig
n
a
l c
la
s
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ifica
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n
a
lg
o
r
ith
m
(
N
a
b
ee
l A
a
d
La
fta
)
505
7
.
3
.
SNR
F
o
r
th
e
ef
f
ec
ted
o
f
SN
R
o
n
DOA
est
i
m
a
tio
n
an
d
,
w
e
w
i
ll
r
ep
ea
t
th
e
p
r
ev
io
u
s
s
i
m
u
la
tio
n
s
in
s
ec
tio
n
(6
)
w
ith
th
e
s
a
m
e
d
ata,
an
d
a
ch
an
g
e
t
h
e
r
ate
o
f
SNR
to
(
-
3
0
d
B
,
0
d
B
an
d
3
0
d
B
)
.
Fig
u
r
e
1
1
s
h
o
w
n
th
e
r
es
u
lt
s
.
As
s
h
o
w
n
i
n
Fi
g
u
r
e
1
1
,
t
h
at
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h
e
lo
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u
es
o
f
SNR
ar
e
in
ac
c
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ate
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es
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lt o
f
MU
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al
g
o
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it
h
m
w
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ated
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an
d
at
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cr
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e
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ig
n
al
o
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es
ti
m
a
te
b
an
d
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ec
o
m
e
s
n
ar
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o
w
,
r
es
u
lti
n
g
in
cr
ea
s
i
n
g
t
h
e
ac
c
u
r
ac
y
o
f
t
h
e
MU
SIC
al
g
o
r
ith
m
.
Fig
u
r
e
9
.
Nu
m
b
er
o
f
ele
m
e
n
t
af
f
ec
ted
Fig
u
r
e
1
0
.
Sp
ac
in
g
ele
m
en
t a
f
f
ec
ted
Fig
u
r
e
1
1
.
SNR
a
f
f
ec
ted
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
1
,
Feb
r
u
ar
y
2021
:
498
-
507
506
8.
CO
NCLU
SI
O
N
I
n
th
i
s
p
ap
er
w
e
e
x
a
m
in
ed
t
h
e
n
o
n
-
GP
S
p
o
s
itio
n
in
g
s
y
s
t
e
m
f
o
r
(
W
SNs
)
lo
ca
lizatio
n
an
d
test
ed
th
e
ef
f
ec
ti
v
e
n
ess
o
f
u
s
i
n
g
th
e
MU
SIC
alg
o
r
ith
m
to
ca
lcu
late
th
e
ar
r
iv
in
g
an
g
le
o
f
s
e
n
s
o
r
s
ig
n
al
b
u
t
t
h
i
s
alg
o
r
ith
m
f
ailed
to
d
etec
tio
n
co
h
er
en
t
s
ig
n
als
an
d
t
h
is
is
a
w
ea
k
p
o
in
t
i
n
t
h
e
p
er
f
o
r
m
an
c
e
o
f
th
is
al
g
o
r
ith
m
esp
ec
iall
y
i
n
lar
g
e
an
d
i
m
p
o
r
tan
t
n
et
w
o
r
k
s
.
T
h
e
r
esear
ch
s
h
o
w
ed
h
o
w
to
d
ev
elo
p
th
is
alg
o
r
ith
m
to
m
o
d
if
ie
d
MU
SIC
al
g
o
r
ith
m
n
a
m
ed
M
-
MU
SI
C
al
g
o
r
ith
m
w
h
ich
h
a
s
p
r
o
v
en
h
ig
h
l
y
e
f
f
ec
t
iv
e
i
n
d
etec
tin
g
w
ir
eles
s
s
en
s
o
r
s
ig
n
al
s
an
d
e
s
ti
m
ated
th
e
DO
A
an
d
p
r
o
v
ed
s
u
cc
es
s
f
u
l
to
d
etec
tio
n
b
o
th
co
h
er
en
t
an
d
n
o
n
-
co
h
er
en
t
s
ig
n
al
s
b
y
u
s
ed
M
A
T
L
A
B
s
i
m
u
latio
n
p
r
o
g
r
a
m
.
T
h
er
ef
o
r
e,
th
e
d
esi
g
n
n
o
n
-
GP
S
lo
ca
l
izatio
n
s
y
s
te
m
f
o
r
(
W
SNs
)
u
s
i
n
g
M
-
MU
SI
C
al
g
o
r
ith
m
p
r
o
d
u
ce
s
a
h
i
g
h
l
y
e
f
f
i
cien
t
lo
ca
lizatio
n
s
y
s
te
m
.
I
t
m
u
s
t
b
e
n
o
ted
th
at
to
en
h
a
n
ce
m
en
t
th
e
r
es
u
lt
s
m
u
s
t
b
e
ch
o
s
e
lar
g
e
n
u
m
b
er
o
f
ele
m
e
n
ts
o
f
(
U
L
A
)
an
d
i
n
cr
ea
s
in
g
t
h
e
d
is
tan
c
e
b
et
w
ee
n
th
e
ele
m
en
ts
o
f
th
e
a
n
ten
n
a
n
o
t
ex
ce
ed
h
al
f
t
h
e
w
av
e
len
g
t
h
an
d
(
SNR
)
at
h
ig
h
r
ate
th
at
ca
n
g
u
ar
a
n
tee
a
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
A
n
to
n
io
a
n
d
M
.
M
a
rin
h
o
,
“
A
r
ra
y
P
ro
c
e
ss
in
g
T
e
c
h
n
iq
u
e
s
f
o
r
Di
re
c
ti
o
n
o
f
A
rriv
a
l
Est
i
m
a
ti
o
n
,
Co
m
m
u
n
ica
ti
o
n
s,
a
n
d
L
o
c
a
li
z
a
ti
o
n
in
V
e
h
ic
u
lar an
d
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
,
”
Do
c
to
ra
l
T
h
e
sis,
Ha
lm
sta
d
Un
iv
e
rsit
y
,
2
0
1
8
.
[2
]
S
.
S
h
a
rm
a
,
“
A
S
tate
o
f
Art
o
n
En
e
rg
y
Eff
i
c
ien
t
M
u
lt
i
p
a
th
R
o
u
t
in
g
i
n
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
fo
rm
a
ti
c
s
a
n
d
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
,
n
o.
3,
pp
.
1
1
1
-
1
1
6
,
2
0
1
8
.
[3
]
A
.
A
.
Ka
m
il
,
e
t
a
l.
,
“
De
sig
n
a
n
d
im
p
le
m
e
n
tatio
n
o
f
g
rid
b
a
se
d
c
lu
ste
rin
g
in
W
S
N
u
sin
g
d
y
n
a
m
ic
sin
k
n
o
d
e
,
”
Bu
ll
e
ti
n
o
f
El
e
c
trica
l
E
n
g
in
e
e
rin
g
a
n
d
I
n
fo
rm
a
t
ics
,
v
o
l.
9
,
n
o
.
5
,
p
p
.
2
0
5
5
-
2
0
6
4
,
2
0
2
0
.
[4
]
A
.
Y.
Ard
ian
sy
a
h
a
n
d
R.
S
a
rn
o
,
“
P
e
rf
o
rm
a
n
c
e
a
n
a
l
y
sis
o
f
w
irele
ss
se
n
so
r
n
e
tw
o
rk
w
it
h
lo
a
d
b
a
l
a
n
c
in
g
f
o
r
d
a
ta
tran
sm
issio
n
u
sin
g
x
b
e
e
z
b
m
o
d
u
le,”
In
d
o
n
e
s
ia
n
J
o
u
rn
a
l
o
f
El
e
c
tr
ica
l
En
g
i
n
e
e
rin
g
a
n
d
Co
mp
u
t
er
S
c
i
e
n
c
e
(
IJ
EE
CS
)
,
v
o
l.
1
8
,
n
o
.
1
,
p
p
.
8
8
-
1
0
0
,
2
0
1
9
.
[5
]
K.
W
.
A
l
-
A
n
i,
e
t
a
l.
,
“
A
n
o
v
e
r
v
ie
w
o
f
w
ir
e
les
s
s
e
n
so
r
n
e
tw
o
rk
a
n
d
it
s
a
p
p
li
c
a
ti
o
n
s,”
In
d
o
n
e
s
ia
n
J
o
u
rn
a
l
o
f
El
e
c
tr
ica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
t
er
S
c
i
e
n
c
e
(
IJ
EE
CS
)
,
v
o
l.
1
7
,
n
o
.
3
,
p
p
.
1
4
8
0
-
1
4
8
6
,
2
0
1
9
.
[6
]
R.
Du
,
e
t
a
l.
,
“
T
h
e
S
e
n
sa
b
le
Cit
y
:
A
S
u
rv
e
y
o
n
th
e
De
p
lo
y
m
e
n
t
a
n
d
M
a
n
a
g
e
m
e
n
t
f
o
r
S
m
a
rt
Cit
y
M
o
n
it
o
ri
n
g
,
”
IEE
E
Co
mm
u
n
ica
t
io
n
s
S
u
rv
e
y
s a
n
d
T
u
t
o
ria
ls
,
v
o
l.
2
1
,
n
o
.
2
,
p
p
.
1
5
3
3
-
1
5
6
0
,
2
0
1
9
.
[7
]
A
.
G
.
Qu
o
c
,
e
t
a
l.
,
“
F
lex
ib
le
c
o
n
f
ig
u
ra
ti
o
n
o
f
w
irele
ss
se
n
so
r
n
e
two
rk
f
o
r
m
o
n
it
o
ri
n
g
o
f
ra
in
f
a
ll
-
in
d
u
c
e
d
lan
d
sl
id
e
,
”
In
d
o
n
e
s
ia
n
J
o
u
r
n
a
l
o
f
El
e
c
tr
ica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
t
er
S
c
i
e
n
c
e
(
I
J
EE
CS
)
,
v
o
l.
1
2
,
n
o
.
3
,
p
p
.
1
0
3
0
-
1
0
3
6
,
2
0
1
8
.
[
8
]
N
.
N
.
A
.
N
a
z
r
i
,
e
t
a
l
.
,
“
B
a
c
k
t
r
a
c
k
i
n
g
s
e
a
r
c
h
o
p
t
i
m
i
z
a
t
i
o
n
f
o
r
c
o
l
l
a
b
o
r
a
t
i
v
e
b
e
a
m
f
o
r
m
i
n
g
i
n
w
i
r
e
l
e
s
s
s
e
n
s
o
r
n
e
t
w
o
r
k
s
,
”
T
E
L
K
O
M
N
I
K
A
(
T
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
C
o
m
p
u
t
i
n
g
E
l
e
c
t
r
o
n
i
c
s
a
n
d
C
o
n
t
r
o
l
)
,
v
o
l
.
1
6
,
n
o
.
4
,
p
p
.
1
8
0
1
-
1
8
0
8
,
2
0
1
8
.
[9
]
B.
T
h
o
e
n
,
e
t
a
l.
,
“
Im
p
ro
v
in
g
A
o
A
lo
c
a
li
z
a
ti
o
n
a
c
c
u
ra
c
y
in
w
irele
ss
a
c
o
u
stic
se
n
so
r
n
e
tw
o
rk
s
w
it
h
a
n
g
u
lar
p
ro
b
a
b
il
it
y
d
e
n
sity
f
u
n
c
ti
o
n
s
,
”
S
e
n
so
rs
(
S
wit
ze
rla
n
d
)
,
v
o
l.
1
9
,
n
o
.
4
,
2
0
1
9
.
[1
0
]
R.
M
.
Bu
e
h
re
r,
e
t
a
l.
,
“
Co
l
lab
o
ra
ti
v
e
S
e
n
so
r
Ne
tw
o
rk
L
o
c
a
li
z
a
ti
o
n
:
A
lg
o
rit
h
m
s
a
n
d
P
ra
c
ti
c
a
l
Iss
u
e
s
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
IEE
E
,
v
o
l.
1
0
6
,
n
o
.
6
,
p
p
.
1
0
8
9
-
1
1
1
4
,
2
0
1
8
.
[1
1
]
S.
S.
Hre
sh
e
e
,
“
L
o
c
a
ti
n
g
th
e
S
e
n
s
o
rs P
o
sit
io
n
s
in
W
S
N
Ba
se
d
o
n
M
USIC
A
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
En
g
i
n
e
e
rin
g
T
e
c
h
n
o
lo
g
ies
(
ICENT
E’1
9
),
p
p
.
2
2
9
-
2
3
5
,
2
0
1
9
.
[1
2
]
S
.
H.
T
h
imm
a
iah
a
n
d
G
.
M
a
h
a
d
e
v
a
n
,
“
A
ra
n
g
e
b
a
se
d
lo
c
a
li
z
a
ti
o
n
e
rro
r
m
in
i
m
iza
ti
o
n
tec
h
n
iq
u
e
f
o
r
w
irele
ss
s
e
n
so
r
n
e
tw
o
rk
,
”
In
d
o
n
e
s
i
a
n
J
o
u
rn
a
l
o
f
El
e
c
tr
ica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
t
er
S
c
i
e
n
c
e
,
v
o
l.
7
,
n
o
.
2
,
p
p
.
3
9
5
-
4
0
3
,
2
0
1
7
.
[1
3
]
J.
X
iao
,
e
t
a
l.
,
“
Re
se
a
rc
h
o
f
T
DO
A
b
a
se
d
se
l
f
-
lo
c
a
li
z
a
ti
o
n
a
p
p
ro
a
c
h
in
w
irele
ss
s
e
n
so
r
n
e
tw
o
rk
,
”
IEE
E
In
t
e
rn
a
t
io
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
tel
l
ig
e
n
t
Ro
b
o
t
s
a
n
d
S
y
st
e
ms
,
p
p
.
2
0
3
5
-
2
0
4
0
,
2
0
0
6
.
[1
4
]
Y.
W
a
n
g
a
n
d
Y.
W
u
,
“
A
n
e
ff
icie
n
t
se
m
id
e
f
in
it
e
re
lax
a
ti
o
n
a
lg
o
rit
h
m
f
o
r
m
o
v
in
g
so
u
rc
e
lo
c
a
li
z
a
ti
o
n
u
si
n
g
T
DO
A
a
n
d
F
DO
A
m
e
a
su
re
m
e
n
ts
,
”
IEE
E
Co
mm
u
n
ica
ti
o
n
s
L
e
tt
e
rs
,
v
o
l
.
2
1
,
n
o
.
1
,
p
p
.
8
0
-
8
3
,
2
0
1
7
.
[1
5
]
K.
Jih
o
o
n
,
e
t
a
l.
,
“
RS
S
se
lf
-
c
a
li
b
ra
ti
o
n
p
r
o
to
c
o
l
f
o
r
W
S
N
lo
c
a
li
z
a
ti
o
n
,
”
2
0
0
7
2
n
d
In
t
e
rn
a
ti
o
n
a
l
S
y
mp
o
si
u
m
o
n
W
ire
l
e
ss
Per
v
a
siv
e
Co
mp
u
t
i
n
g
,
p
p
.
1
8
1
-
1
8
4
,
2
0
0
7
.
[1
6
]
A
.
Zh
a
n
g
,
e
t
a
l.
,
“
P
o
i
n
t
in
tr
ian
g
le
tes
ti
n
g
b
a
se
d
tri
late
ra
ti
o
n
lo
c
a
li
z
a
ti
o
n
a
lg
o
rit
h
m
in
w
ir
e
les
s
s
e
n
so
r
n
e
tw
o
rk
s
,
”
KS
II
T
ra
n
sa
c
ti
o
n
s
o
n
In
ter
n
e
t
a
n
d
In
f
o
rm
a
ti
o
n
S
y
st
e
ms
,
v
o
l.
6
,
n
o
.
1
0
,
p
p
.
2
5
6
7
-
2
5
8
6
,
2
0
1
2
.
[1
7
]
M
.
G
.
Ka
v
it
h
a
,
e
t
a
l.
,
“
T
ril
a
tera
ti
o
n
b
a
se
d
lo
c
a
li
z
a
ti
o
n
m
e
th
o
d
u
si
n
g
m
o
b
il
e
a
n
c
h
o
r
i
n
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
s in
A
p
p
li
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8
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P
.
Krista
li
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,
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t
a
l.
,
“
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w
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e
ss
se
n
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r
n
e
tw
o
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p
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li
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ti
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n
,
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2
0
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d
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t
e
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l
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in
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f
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rm
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,
p
p
.
2
5
4
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2
5
9
,
2
0
1
6
.
[1
9
]
L
e
e
l
a
v
a
th
y
S
.
R.
a
n
d
S
o
p
h
ia
S.
,
“
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r
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n
g
L
o
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li
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ti
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u
sin
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rian
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s
,
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[2
0
]
G
.
K
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o
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a
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d
J.
A
g
a
r
k
h
e
d
,
“
P
a
tt
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rn
m
a
t
c
h
in
g
in
tru
si
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d
e
tec
ti
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rk
s
,
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d
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e
rn
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io
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a
l
Co
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re
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o
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Ad
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s
in
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e
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e
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tro
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ics
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fo
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t
io
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,
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mm
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d
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p
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2
8
,
2
0
1
6
.
[2
1
]
E.
Din
g
,
e
t
a
l.
,
“
Im
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ro
v
e
d
p
a
tt
e
r
n
m
a
tch
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g
lo
c
a
li
z
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ti
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n
o
f
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S
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in
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o
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l
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,
”
2
0
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n
t
e
rn
a
t
io
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l
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re
n
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o
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In
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rm
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t
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n
Acq
u
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it
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o
n
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)
,
p
p
.
5
3
4
-
5
3
7
,
2
0
0
7
.
[2
2
]
V
.
M
a
d
h
a
v
a
a
n
d
Ja
g
a
d
e
e
sh
a
S
.
,
“
A
Co
m
p
a
r
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ti
v
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tu
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y
o
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D
OA
Esti
m
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m
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i
n
g
U
s
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n
g
K
a
l
m
a
n
F
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l
t
e
r
,
”
S
i
g
n
a
l
and
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m
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g
e
P
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s
s
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l
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1
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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N:
2088
-
8708
Wir
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507
[2
3
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L
.
Wan
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t
a
l.
,
“
Distrib
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ted
DO
A
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sti
m
a
ti
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[2
4
]
S
.
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o
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g
,
e
t
a
l.
,
“
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USIC
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lg
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1
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6
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2
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[2
5
]
M
.
A
.
Ih
e
d
ra
n
e
a
n
d
S
.
Bri,
“
Dir
e
c
ti
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lg
o
rit
h
m
,
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d
o
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e
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J
o
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rn
a
l
o
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En
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rin
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n
d
C
o
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e
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J
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)
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1
,
p
p
.
3
0
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7
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.
[2
6
]
A
.
Hira
ta,
e
t
a
l.
,
“
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sti
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ti
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n
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w
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v
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s
with
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USIC
a
n
d
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m
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,
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EE
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p
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1
9
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3
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3
.
[2
7
]
J.
Ch
e
n
,
e
t
a
l.
,
“
Tw
o
-
d
im
e
n
sio
n
a
l
d
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ti
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rc
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ime
d
e
a
n
sp
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a
rra
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w
it
h
M
USIC
a
lg
o
rit
h
m
,
”
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E
Acc
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ss
,
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l.
6
,
p
p
.
4
9
7
4
0
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4
9
7
4
5
,
2
0
1
8
.
[2
8
]
N.
Kh
a
n
,
e
t
a
l.
,
“
Dire
c
ti
o
n
o
f
a
rr
iv
a
l
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sti
m
a
ti
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n
o
f
so
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rc
e
s
w
it
h
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n
ters
e
c
ti
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g
sig
n
a
tu
re
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n
ti
m
e
–
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re
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e
n
c
y
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o
m
a
in
u
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a
c
o
m
b
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ti
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o
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e
stim
a
ti
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n
a
n
d
M
USIC
a
lg
o
rit
h
m
,
”
M
u
lt
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n
sio
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l
S
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d
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ig
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,
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l.
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1
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.
2
,
p
p
.
5
4
9
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6
7
,
2
0
2
0
.
[2
9
]
T
.
W
a
n
g
,
e
t
a
l.
,
“
Co
n
ti
n
u
o
u
s
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r
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d
b
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n
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li
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t
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HF
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a
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p
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n
g
a
rra
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sin
g
M
USIC
a
l
g
o
rit
h
m
,
”
At
mo
s
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h
e
ric
Res
e
a
rc
h
,
v
o
l.
2
3
1
,
2
0
2
0
.
[3
0
]
E.
Kw
ize
r
a
,
e
t
a
l.
,
“
Dire
c
ti
o
n
o
f
A
rriv
a
l
Esti
m
a
ti
o
n
Ba
se
d
o
n
M
USIC
A
lg
o
rit
h
m
U
sin
g
Un
if
o
r
m
a
n
d
No
n
-
U
n
if
o
rm
L
in
e
a
r
A
rra
y
s
,
”
In
t
e
rn
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ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
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n
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g
Res
e
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rc
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a
n
d
Ap
p
li
c
a
ti
o
n
.
v
o
l.
7
,
n
o
.
3
,
p
p
.
51
-
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2
0
1
7
.
[3
1
]
Z.
Zh
a
n
g
,
“
Dire
c
ti
o
n
o
f
Ra
d
io
F
i
n
d
i
n
g
v
ia M
USIC
(M
u
lt
ip
le S
ig
n
a
l
Clas
si
f
ica
ti
o
n
)
A
lg
o
rit
h
m
f
o
r
Ha
rd
w
a
re
De
si
g
n
S
y
st
e
m
,
”
i
n
J
o
u
rn
a
l
o
f
Ph
y
sic
s
:
Co
n
f
e
re
n
c
e
S
e
rie
s
,
th
e
2
0
1
7
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Clo
u
d
T
e
c
h
n
o
l
o
g
y
a
n
d
Co
mm
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
,
v
o
l.
9
1
0
,
2
0
1
7
.
[3
2
]
B.
Bo
u
sta
n
i,
e
t
a
l.
,
“
T
h
e
im
p
a
c
t
o
f
n
o
ise
o
n
d
e
tec
ti
n
g
th
e
a
rriv
a
l
a
n
g
le
u
sin
g
t
h
e
ro
o
t
-
W
S
F
a
lg
o
rit
h
m
,
”
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
t
io
n
C
o
mp
u
t
i
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tro
l
)
,
v
o
l
.
1
8
,
n
o
.
3
,
p
p
.
1
1
5
0
-
1
1
5
7
,
2
0
2
0
.
[3
3
]
S
.
El
Ba
rra
k
,
e
t
a
l.
,
“
A
p
p
li
c
a
ti
o
n
o
f
M
V
DR
a
n
d
M
USIC
sp
e
c
tr
u
m
se
n
sin
g
tec
h
n
iq
u
e
s
w
it
h
im
p
lem
e
n
tatio
n
o
f
No
d
e
’s
p
ro
t
o
ty
p
e
f
o
r
c
o
g
n
it
iv
e
r
a
d
io
A
d
Ho
c
n
e
t
w
o
rk
s
,
”
Pro
c
e
e
d
in
g
s
o
f
th
e
2
0
1
7
I
n
t
e
rn
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
S
ma
rt Di
g
it
a
l
En
v
ir
o
n
me
n
t
,
p
p
.
1
0
1
-
1
0
6
,
2
0
1
7
.
[3
4
]
J.
Dz
iels
k
i,
e
t
a
l.
,
“
Co
m
m
e
n
ts
o
n
M
o
d
if
ie
d
M
USIC
a
lg
o
rit
h
m
f
o
r
e
sti
m
a
ti
n
g
DO
A
o
f
sig
n
a
ls
,
”
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
5
5
,
n
o
.
2
,
p
p
.
2
5
3
-
2
5
4
,
1
9
9
6
.
[3
5
]
X
.
Jia
o
a
n
d
S
.
S
u
,
“
A
m
e
th
o
d
o
f
2
-
D
DO
A
e
sti
m
a
ti
o
n
b
a
se
d
o
n
m
o
d
if
ied
M
USIC
a
lg
o
rit
h
m
,
”
2
0
1
0
2
n
d
In
t
e
rn
a
t
io
n
a
l
W
o
rk
sh
o
p
o
n
In
tell
i
g
e
n
t
S
y
st
e
ms
a
n
d
Ap
p
l
ica
t
io
n
s
,
p
p
.
1
-
4
,
2
0
1
0
.
[3
6
]
Y
.
G
a
o
,
e
t
a
l.
,
“
A
n
i
m
p
ro
v
e
d
M
USIC
a
lg
o
rit
h
m
f
o
r
DO
A
e
sti
m
a
ti
o
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