Efficient 2D-DOA Estimation Based on Triple Attention Mechanism for L-Shaped Array
Yonghong Zhao,
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Xiumei Fan,
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Jun S. Liu
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et al.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(8), P. 2359 - 2359
Published: April 8, 2025
Accurate
direction-of-arrival
(DOA)
estimation
is
crucial
to
a
variety
of
applications,
including
wireless
communications,
radar
systems,
and
sensor
arrays.
In
this
work,
we
propose
novel
deep
convolutional
neural
network
(DCN)
called
TADCN
for
2D-DOA
using
an
L-shaped
array.
The
achieves
high
performance
through
triple
attention
mechanism
(TAM).
Specifically,
the
new
architecture
enables
capture
relationships
across
channel,
height,
width
dimensions
signal
sample
features,
thereby
enhancing
feature
extraction
capability
improving
resulting
spatial
spectrum.
To
end,
spectrum
processed
by
proposed
analyzer
yield
high-precision
DOA
results.
An
automatic
angle
matching
method
based
on
employed
estimating
pairing
between
estimated
azimuth
elevation
sets.
Furthermore,
overall
efficiency
enhanced
parallel
processing
networks.
Simulation
results
demonstrate
that
algorithm
outperforms
traditional
methods
learning-based
approaches
various
noise
levels
snapshots
while
maintaining
better
even
in
presence
correlated
sources.
Language: Английский
Twin proximal support vector regression with heteroscedastic Gaussian noise
Chao Liu,
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Quan Qian
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Expert Systems with Applications,
Journal Year:
2024,
Volume and Issue:
250, P. 123840 - 123840
Published: March 26, 2024
Language: Английский
Direction of Arrival Estimation Based on DNN and CNN
Wu Cao,
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Wen Ren,
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Zhenyu Zhang
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et al.
Electronics,
Journal Year:
2024,
Volume and Issue:
13(19), P. 3866 - 3866
Published: Sept. 29, 2024
The
accuracy
of
Direction
Arrival
(DOA)
estimation
primarily
depends
on
the
precision
data.
When
receiver
uses
a
low-precision
analog-to-digital
converter
(ADC),
traditional
DOA
algorithms
exhibit
poor
accuracy.
To
face
challenge
multi-target
in
scenarios
with
ADC
quantized
sampling,
this
paper
proposes
novel
algorithm
for
signals
based
classification
problems.
A
deep
learning
network
was
constructed
using
Deep
Neural
Networks
(DNNs)
and
Convolutional
(CNNs),
divided
into
signal
recovery
framework
framework.
DNN
is
utilized
to
recover
that
have
undergone
quantization,
while
CNN
addresses
problem
estimate
from
received
data
an
unknown
number
sources.
comprehensive
analysis
impact
signal-to-noise
ratio
(SNR),
array
elements,
quantization
bits
proposed
conducted.
Simulation
results
indicate
exhibits
superior
performance
scenarios,
characterized
by
reduced
computational
complexity,
thereby
facilitating
real-time
estimation.
Language: Английский
Soft sensor modeling of steel pickling concentration based on IGEP algorithm
Li Wang,
No information about this author
Y. Xin,
No information about this author
Xunyang Gao
No information about this author
et al.
Canadian Metallurgical Quarterly,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 13
Published: Nov. 26, 2024
Accurate
measurement
of
acid
concentration
is
paramount
for
ensuring
the
quality
strip
steel
pickling.
Online
measurement,
a
method
that
reduces
operational
complexity
and
lags
effectively,
gradually
replacing
offline
concentration.
In
this
study,
an
indirect
soft
sensor
model
based
on
improved
gene
expression
programming
(IGEP)
algorithm
has
been
constructed,
leveraging
easily
measurable
indexes
from
large-scale
dataset.
The
IGEP-based
predicted
mean
absolute
errors
H+
Fe2+
concentrations
were
1.72
1.98
g/L,
respectively.
Additionally,
goodness
fit
values
prediction
models
0.945
0.933,
Compared
with
support
vector
regression
(SVR),
which
suitable
small
samples,
it
was
demonstrated
achieved
better
predictive
performance.
Taken
together,
our
study
designed
more
effective
practical
determining
pickling,
providing
new
ideal
choice
industry,
profound
significance
in
control
pickling
solution
production
steel.
Language: Английский