Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Weiliang Zuo; Jingmin Xin; Changnong Liu; Nanning Zheng; Akira Sano

doi:10.1109/JAS.2023.123549

Volume 10 Issue 8

Aug. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(8): 1716-1729

W. L. Zuo, J. M. Xin, C. N. Liu, N. N. Zheng, and A. Sano, “Improved Capon estimator for high-resolution DOA estimation and its statistical analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1716–1729, Aug. 2023. doi: 10.1109/JAS.2023.123549

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W. L. Zuo, J. M. Xin, C. N. Liu, N. N. Zheng, and A. Sano, “Improved Capon estimator for high-resolution DOA estimation and its statistical analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1716–1729, Aug. 2023. doi: 10.1109/JAS.2023.123549

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

doi: 10.1109/JAS.2023.123549

Funds: This work was supported in part by the National Natural Science Foundation of China (62201447) and the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-640). This paper was presented in part at 2014 Int. Symposium Nonlinear Theory and Its Applications (NOLTA’2014), Luzern, Switzerland, Sept. 14–18, 2014

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Author Bio:
Weiliang Zuo (Member, IEEE) received the B.E. degree in electrical engineering and the Ph.D. degree in control science and engineering from Xi’an Jiaotong University in 2010 and 2018, respectively.During 2016 to 2017, he was a visiting Ph.D. candidate ar the School of Electrical and Computer Engineering, Georgia Institute of Technology (Georgia Tech). He is currently an Associate Professor at the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. His current research interests include array and statistical signal processing, pattern recognition, and medical imaging processing

Jingmin Xin (Senior Member, IEEE) received the B.E. degree in information and control engineering from Xi’an Jiaotong University, in 1988, and the M.S. and Ph.D. degrees in electrical engineering from Keio University, Japan, in 1993 and 1996, respectively.From 1988 to 1990, he was with the Tenth Institute of Ministry of Posts and Telecommunications (MPT) of China. He was with the Communications Research Laboratory, Japan, as an Invited Research Fellow of the Telecommunications Advancement Organization of Japan (TAO) from 1996 to 1997 and as a Postdoctoral Fellow of the Japan Science and Technology Corporation (JST) from 1997 to 1999. He was also a Guest (Senior) Researcher with YRP Mobile Telecommunications Key Technology Research Laboratories Company, Limited, Japan, from 1999 to 2001. From 2002 to 2007, he was with Fujitsu Laboratories Limited, Japan. Since 2007, he has been a Professor at Xi’an Jiaotong University. His research interests include adaptive filtering, statistical and array signal processing, system identification, and pattern recognition

Changnong Liu received the B.S. degree in automation in 2012 and the M.S. degree in control science and engineering (pattern recognition and intelligence systems) in 2015 from Xi’an Jiaotong University. Currently she is an Application System Administrator at the Data Center of China Construction Bank

Nanning Zheng (Fellow, IEEE) graduated from the Department of Electrical Engineering, Xi’an Jiaotong University in 1975, and received the M.S. degree in information and control engineering from Xi’an Jiaotong University in 1981 and the Ph.D. degree in electrical engineering from Keio University, Japan, in 1985.He joined Xi’an Jiaotong University in 1975, and is currently a Professor and the Director of the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. His research interests include computer vision, pattern recognition and image processing, and hardware implementation of intelligent systems.Dr. Zheng became a Member of the Chinese Academy of Engineering in 1999, and he is the Chinese Representative on the Governing Board of the International Association for Pattern Recognition. He also serves as an Executive Deputy Editor of the Chinese Science Bulletin

Akira Sano (Member, IEEE) received the B.E., M.S., and Ph.D. degrees in mathematical engineering and information physics from the University of Tokyo, Japan, in 1966, 1968, and 1971, respectively.In 1971, he joined the Department of Electrical Engineering, Keio University, Japan, where he was a Professor with the Department of System Design Engineering till 2009. He is currently Professor Emeritus of Keio University. He has been a Member of Science Council of Japan since 2005. He was a Visiting Research Fellow at the University of Salford, U.K., from 1977 to 1978. He is the coauthor of the textbook State Variable Methods in Automatic Control (Wiley, 1988). His current research interests include adaptive modeling and design theory in control, signal processing and communication, and applications to control of sounds and vibrations, mechanical systems, and mobile communication systems.Dr. Sano is a Fellow of the Society of Instrument and Control Engineers and is a Member of the Institute of Electrical Engineering of Japan and the Institute of Electronics, Information and Communications Engineers of Japan. He was General Co-Chair of 1999 IEEE Conference of Control Applications and an IPC Chair of 2004 IFAC Workshop on Adaptation and Learning in Control and Signal Processing. He was the Chair of IFAC Technical Committee on Modeling and Control of Environmental Systems from 1996 to 2001. He has also been Vice Chair of IFAC Technical Committee on Adaptive Control and Learning since 1999 and the Chair of IFAC Technical Committee on Adaptive and Learning Systems since 2002. He was also on the Editorial Board of Signal Processing. He was the recipient of the Kelvin Premium from the Institute of Electrical Engineering in 1986
Corresponding author: Jingmin Xin, e-mail: jxin@mail.xjtu.edu.cn
Received Date: 2022-12-30
Accepted Date: 2023-03-09

Available Online: 2023-06-12

Abstract

Abstract

Despite some efforts and attempts have been made to improve the direction-of-arrival (DOA) estimation performance of the standard Capon beamformer (SCB) in array processing, rigorous statistical performance analyses of these modified Capon estimators are still lacking. This paper studies an improved Capon estimator (ICE) for estimating the DOAs of multiple uncorrelated narrowband signals, where the higher-order inverse (sample) array covariance matrix is used in the Capon-like cost function. By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC (multiple signal classification), it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE, the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio (SNR). Furthermore the statistical performance of the ICE is analyzed, and the large-sample mean-squared-error (MSE) expression of the estimated DOA is derived. Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples, where the Cramer-Rao lower bound (CRB) is used to evaluate the validity of the derived asymptotic MSE expression.
- Capon beamformer,
- direction-of-arrival (DOA) estimation,
- large-sample mean-squared-error (MSE),
- subspace-based methods,
- uniform linear array

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References(112)

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沈阳化工大学材料科学与工程学院沈阳 110142

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Higher-order inverse array covariance matrix based improved Capon DOA estimation
ICE and MUSIC are equivalent regardless of the SNR with large power order
The asymptotic MSE expressions of DOA estimates are derived explicitly

Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

doi: 10.1109/JAS.2023.123549

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