Distance estimation method measurable from 0m based on standing wave using band-limited sound with uniform amplitude and random phase

2010 ◽  
Author(s):  
Keiji Kawanishi ◽  
Noboru Nakasako ◽  
Toshihiro Shinohara ◽  
Tetsuji Uebo
Keyword(s):  
Standing Wave ◽  
Random Phase ◽  
Estimation Method ◽  
Distance Estimation ◽  
Band Limited

scholarly journals Distance measurement based on standing wave for band-limited audible sound with random phase

2009 ◽  
Vol 30 (1) ◽  
pp. 18-24 ◽  
Author(s):  
Tetsuji Uebo ◽  
Noboru Nakasako ◽  
Norimitsu Ohmata ◽  
Atsushi Mori
Keyword(s):  
Standing Wave ◽  
Random Phase ◽  
Distance Measurement ◽  
Audible Sound ◽  
Band Limited

A Trial on Implementation of Distance Estimation Method Based on Standing Wave of Audible Sound

2009 ◽  
Vol 129 (2) ◽  
pp. 314-319 ◽  
Author(s):  
Norimitsu Ohmata ◽  
Tetsuji Uebo ◽  
Noboru Nakasako ◽  
Toshihiro Shinohara
Keyword(s):  
Standing Wave ◽  
Estimation Method ◽  
Distance Estimation ◽  
Audible Sound

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

Distance Estimation Method Based on Photon Counting LIDAR

2015 ◽  
Vol 44 (5) ◽  
pp. 501002
Author(s):  
尹文也 YIN Wen-ye ◽  
石峰 SHI Feng ◽  
何伟基 HE Wei-ji ◽  
顾国华 GU Guo-hua ◽  
陈钱 CHEN Qian
Keyword(s):  
Photon Counting ◽  
Estimation Method ◽  
Distance Estimation

Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection

2020 ◽  
Vol 69 (5) ◽  
pp. 4907-4919 ◽  
Author(s):  
Ting Zhe ◽  
Liqin Huang ◽  
Qiang Wu ◽  
Jianjia Zhang ◽  
Chenhao Pei ◽  
...  
Keyword(s):  
Estimation Method ◽  
Distance Estimation ◽  
Monocular Vision

Multi-hop distance estimation method based on regulated neighbourhood measure

2012 ◽  
Vol 6 (13) ◽  
pp. 2084-2090 ◽  
Author(s):  
G. Wu ◽  
S. Wang ◽  
Y. Dong ◽  
B. Wang
Keyword(s):  
Estimation Method ◽  
Distance Estimation

Position adjustment method and distance estimation method of magnetic field supply and detection unit for magnetic hyperthermia

2017 ◽  
Vol 12 ◽  
pp. S3-S9 ◽  
Author(s):  
Loi Tonthat ◽  
Fumitaka Aki ◽  
Eiki Matsuda ◽  
Hajime Saito ◽  
Noboru Yoshimura ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Estimation Method ◽  
Distance Estimation ◽  
Magnetic Hyperthermia ◽  
Adjustment Method ◽  
Detection Unit

Inertial cavitation thresholds during standing wave disruption using random phase modulation.

2009 ◽  
Vol 126 (4) ◽  
pp. 2174
Author(s):  
Caleb H. Farny ◽  
Sai Chun Tang ◽  
Greg T. Clement
Keyword(s):  
Standing Wave ◽  
Phase Modulation ◽  
Random Phase ◽  
Inertial Cavitation

Enhanced Filter-Based Relative Distance Estimation Method

2016 ◽  
Vol 22 (9) ◽  
pp. 2496-2499
Author(s):  
Sang-Geol Lee ◽  
Yunsick Sung ◽  
Young-Sik Jeong
Keyword(s):  
Estimation Method ◽  
Distance Estimation ◽  
Relative Distance
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