Comparing the two proposed methods, SVDR performed
better than SVR with negative ∆RCS. This difference arises
from the difference in how to deal with the singular value
ratio information. The proposed methods can accurately estimate because the check phase starts from i = imax and estimation failure decreases.
Figure 11 shows the estimation success rate as a function of the number of sources in case of Scenario #6 in Table 2. The proposed methods outperformed AIC and MDL
for all the numbers of sources. These results indicate that
the proposed methods can accurately estimate more sources
than AIC and MDL even when the environment is challenging, in which AIC or MDL cannot perform well. However,
the success rates of the proposed methods degraded as the
number of sources increases. This is caused by an increase
in the check phase for the singular value ratio with Th2 .
Further studies for improving estimation performance with
more sources should be conducted.
Figure 12 shows the evaluation for estimation success
rate as a function of the downsampling rate W. The value of
W was changed from 21 to 27 , where the maximum value is
same to the number of snapshots N = 27 = 128. The estimation performances of the proposed methods improved as
W increases. This is because the larger downsampling rate
enables the use of a strict bandpass filter that has a strong
denoising effect. However, the proposed methods had the
worst estimation performances when W = 27 because of too
much downsampling effect which affects to the success rate.
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Shohei Hamada
received B.E. and M.E.
degrees in Electrical and Computer Engineering from Yokohama National University in 2019
and 2021, respectively. He is with Denso Corporation since 2021. His research interests include
MIMO radar system and array signal processing.
Koichi Ichige
received B.E., M.E., and
Dr. Eng. degrees in Electronics and Computer
Engineering from the University of Tsukuba
in 1994, 1996, and 1999. He became a research associate at the Department of Electrical and Computer Engineering, Yokohama National University in 1999, where he is currently
a professor. He was a visiting researcher at
the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland, in 2001–2002. His
research interests include digital signal processing, approximation theory, and their applications to image processing and
mobile communication. He served as an associate editor of IEEE Transactions on Industrial Electronics in 2004–2008, an associate editor of Journal
of Circuits, Systems and Computers (JCSC) in 2012–2014, an associate
editor of IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (IEICE-EA) in 2015–2018, and an editor
of IEICE-EA in 2019–2021. He is a member of IEEE and IEICE.
Katsuhisa Kashiwagi
received the B.E.
and M.E. degrees in Quantum Engineering and
Systems Science from the University of Tokyo
in 1999 and 2001, respectively. He is currently
with Murata Manufacturing Co., Ltd. for the development of radar sensors. His research topics
are radar system design, radar signal processing
as well as integrated circuit.
Nobuya Arakawa
received B.E. and M.E.
degrees in Electrical and Computer Engineering from Yokohama National University in 2015
and 2017, respectively. He joined Murata Manufacturing Co., Ltd. in 2017 and is now with Continental Automotive Corporation. His research
interests include millimeter wave wireless communications and automotive radar.
Ryo Saito
received B.E. and M.E. degrees in Electrical and Computer Engineering
from Yokohama National University in 2017
and 2019, respectively. He joined Murata Manufacturing Co., Ltd. and is currently working
on research of millimeter wave wireless communications. His research interests include
FMCW-MIMO radar and location estimation
techniques.
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