DOA Estimation of Two Targets with Deep Learning

2018 ◽  
Author(s):  
Yuya Kase ◽  
Toshihiko Nishimura ◽  
Takeo Ohgane ◽  
Yasutaka Ogawa ◽  
Daisuke Kitayama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doa Estimation

scholarly journals Accuracy Improvement in DOA Estimation with Deep Learning

2021 ◽  
Author(s):  
Yuya KASE ◽  
Toshihiko NISHIMURA ◽  
Takeo OHGANE ◽  
Yasutaka OGAWA ◽  
Takanori SATO ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doa Estimation ◽  
Accuracy Improvement

A Deep Learning Based Mutual Coupling Correction and DOA Estimation Algorithm

2021 ◽  
Author(s):  
Wenwei Fang ◽  
Dingke Yu ◽  
Xin Wang ◽  
Yuzhang Xi ◽  
Zhihui Cao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mutual Coupling ◽  
Estimation Algorithm ◽  
Doa Estimation

Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System

2018 ◽  
Vol 67 (9) ◽  
pp. 8549-8560 ◽  
Author(s):  
Hongji Huang ◽  
Jie Yang ◽  
Hao Huang ◽  
Yiwei Song ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo System ◽  
Super Resolution ◽  
Doa Estimation

scholarly journals Fundamental Trial on DOA Estimation with Deep Learning

2020 ◽  
Vol E103.B (10) ◽  
pp. 1127-1135
Author(s):  
Yuya KASE ◽  
Toshihiko NISHIMURA ◽  
Takeo OHGANE ◽  
Yasutaka OGAWA ◽  
Daisuke KITAYAMA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doa Estimation

scholarly journals Design of sparse arrays via deep learning for enhanced DOA estimation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Steven Wandale ◽  
Koichi Ichige
Keyword(s):  
Deep Learning ◽  
Linear Array ◽  
Antenna Selection ◽  
Computational Cost ◽  
Doa Estimation ◽  
Classification Problem ◽  
Training Data ◽  
Sparse Linear Array ◽  
Selection Approach ◽  
Selection For

AbstractThis paper introduces an enhanced deep learning-based (DL) antenna selection approach for optimum sparse linear array selection for direction-of-arrival (DOA) estimation applications. Generally, the antenna selection problem yields a combination of subarrays as a solution. Previous DL-based methods designated these subarrays as classes to fit the problem into a classification problem to which a convolutional neural network (CNN) is employed to solve it. However, these methods sample the combination set randomly to reduce computational cost related to the generation of training data, and it often leads to sub-optimal solutions due to ill-sampling issues. Hence, in this paper, we propose an improved DL-based method by constraining the combination set to retain the hole-free subarrays to enhance the method’s performance and sparse subarrays rendered. Numerical examples show that the proposed method yields sparser subarrays with better beampattern properties and improved DOA estimation performance than conventional DL techniques.

Performance Analysis of DOA Estimation of Two Targets Using Deep Learning

2019 ◽  
Author(s):  
Yuya Kase ◽  
Toshihiko Nishimura ◽  
Takeo Ohgane ◽  
Yasutaka Ogawa ◽  
Daisuke Kitayama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Analysis ◽  
Doa Estimation
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