The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이득/위상 불확실성이 있는 희소 수신 어레이에 대한 압축 감지(CS)를 사용하는 적응형 디지털 빔형성 기술을 위한 새로운 방법이 제시됩니다. 도착하는 신호의 희박성 때문에 CS 이론을 채택하여 더 적은 데이터로 수신 신호를 샘플링하고 복구할 수 있습니다. 그러나 이득/위상 불확실성으로 인해 신호의 희박한 표현은 최적이 아닙니다. 희소 표현에 대한 이득/위상 불확실성의 영향을 제거하기 위해 대부분의 현재 연구는 이득/위상 불확실성을 먼저 교정하는 데 중점을 둡니다. 이득/위상 불확실성의 영향을 극복하기 위해 본 논문에서는 TLS(Total Least Square) 알고리즘을 기반으로 한 새로운 사전 최적화 방법을 제안합니다. 이득/위상 불확실성이 있는 어레이 신호 수신 모델을 EIV 모델로 전송하여 이득/위상 불확실성 효과를 추가 오류 행렬로 처리합니다. 본 논문에서 제안하는 방법은 CS 신호 재구성 알고리즘을 사용하여 희소 계수를 추정하고, 이득/위상 불확실성이 있는 오류 행렬을 업데이트하기 위해 TLS 방법을 사용하여 데이터를 재구성합니다. 시뮬레이션 결과는 희소 정규화된 전체 최소 제곱 알고리즘이 이득/위상 불확실성의 영향으로 수신 신호를 더 잘 복구할 수 있음을 보여줍니다. 그런 다음 적응형 디지털 빔형성 알고리즘을 채택하여 복구된 데이터를 사용하여 안테나 빔을 형성합니다.
Bin HU
Ministry of Industry and Information Technology,Harbin Institute of Technology
Xiaochuan WU
Ministry of Industry and Information Technology,Harbin Institute of Technology
Xin ZHANG
Ministry of Industry and Information Technology,Harbin Institute of Technology
Qiang YANG
Ministry of Industry and Information Technology,Harbin Institute of Technology
Di YAO
Ministry of Industry and Information Technology,Harbin Institute of Technology
Weibo DENG
Ministry of Industry and Information Technology,Harbin Institute of Technology
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Bin HU, Xiaochuan WU, Xin ZHANG, Qiang YANG, Di YAO, Weibo DENG, "Adaptive Beamforming Based on Compressed Sensing with Gain/Phase Uncertainties" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 8, pp. 1257-1262, August 2018, doi: 10.1587/transfun.E101.A.1257.
Abstract: A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1257/_p
부
@ARTICLE{e101-a_8_1257,
author={Bin HU, Xiaochuan WU, Xin ZHANG, Qiang YANG, Di YAO, Weibo DENG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Beamforming Based on Compressed Sensing with Gain/Phase Uncertainties},
year={2018},
volume={E101-A},
number={8},
pages={1257-1262},
abstract={A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.},
keywords={},
doi={10.1587/transfun.E101.A.1257},
ISSN={1745-1337},
month={August},}
부
TY - JOUR
TI - Adaptive Beamforming Based on Compressed Sensing with Gain/Phase Uncertainties
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1257
EP - 1262
AU - Bin HU
AU - Xiaochuan WU
AU - Xin ZHANG
AU - Qiang YANG
AU - Di YAO
AU - Weibo DENG
PY - 2018
DO - 10.1587/transfun.E101.A.1257
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2018
AB - A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.
ER -