There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.
Masanari NOTO
University of Electro-Communications
Fang SHANG
University of Electro-Communications
Shouhei KIDERA
University of Electro-Communications
Tetsuo KIRIMOTO
University of Electro-Communications
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Masanari NOTO, Fang SHANG, Shouhei KIDERA, Tetsuo KIRIMOTO, "Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 6, pp. 1513-1520, June 2018, doi: 10.1587/transcom.2017EBP3324.
Abstract: There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3324/_p
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@ARTICLE{e101-b_6_1513,
author={Masanari NOTO, Fang SHANG, Shouhei KIDERA, Tetsuo KIRIMOTO, },
journal={IEICE TRANSACTIONS on Communications},
title={Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing},
year={2018},
volume={E101-B},
number={6},
pages={1513-1520},
abstract={There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.},
keywords={},
doi={10.1587/transcom.2017EBP3324},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 1513
EP - 1520
AU - Masanari NOTO
AU - Fang SHANG
AU - Shouhei KIDERA
AU - Tetsuo KIRIMOTO
PY - 2018
DO - 10.1587/transcom.2017EBP3324
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2018
AB - There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.
ER -