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2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017
In this paper, a matrix pencil characteristic equation-based source number estimator is proposed. An enhanced matrix is defined through partition-and-stacking process by the original data on the uniform linear array, and its covariance matrix is computed, which is proved to be a toeplitz conjugate symmetry matrix.
Jing Wang, Fei Ji, Fangjiong Chen
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In this paper, a matrix pencil characteristic equation-based source number estimator is proposed. An enhanced matrix is defined through partition-and-stacking process by the original data on the uniform linear array, and its covariance matrix is computed, which is proved to be a toeplitz conjugate symmetry matrix.
Jing Wang, Fei Ji, Fangjiong Chen
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Doa estimation by covariance matrix sparse reconstruction of coprime array
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015In this paper, we propose a direction-of-arrival estimation method by covariance matrix sparse reconstruction of coprime array. Specifically, source locations are estimated by solving a newly formulated convex optimization problem, where the difference between the spatially smoothed covariance matrix and the sparsely reconstructed one is minimized ...
Chengwei Zhou +3 more
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Robust adaptive beamforming based on interference covariance matrix sparse reconstruction
Signal Processing, 2014Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in the training data. In this paper, we reconstruct the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance matrix.
Gu, Yujie +3 more
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Covariance Matrix Reconstruction Using Parsimonious Measurements and Low-sample Support
2020 IEEE Radar Conference (RadarConf20), 2020In this paper, we consider the problem of spacetime adaptive processing (STAP) weight vector design using parsimonious spatial measurements and low temporal sample support. The extreme case when a single space-time data snapshot is the only available data is considered.
Aboulnasr Hassanien, Moeness G. Amin
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A Novel Clutter Covariance Matrix Reconstruction Method for Airborne STAP
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP), 2020The clutter plus noise covariance matrix (CNCM) usually estimated by the training snapshots is the key to obtain the weight vector in space-time adaptive processing (STAP). However, the CNCM is difficult to estimate accurately in small samples, which affects the target estimation seriously.
Mingxin Liu, Lin Zou, Xuegang Wang
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Robust adaptive multichannel SAR processing based on covariance matrix reconstruction
Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 2018With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don’t take the nonuniformity of scattering coefficient into consideration.
Feng He, Zhenya Tan
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RFI Localization via Generalized Augmented Covariance Matrix Reconstruction
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022Jingyu Tao, Dong Zhu, Fei Hu
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Minimum redundancy space-time adaptive processing utilizing reconstructed covariance matrix
2017 IEEE Radar Conference (RadarConf), 2017The maximum space-time degrees of freedom (DOF) is restricted to the number of antennas and pulses. In this paper, a novel sparse space time adaptive processing (STAP) scheme is proposed based on the concept of minimum redundancy arrays.We arrange the array geometry and the temporal sampler interval to make the location of joint space-time samples ...
Ruiyang Li +4 more
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Direction-of-arrival estimation based on Toeplitz covariance matrix reconstruction
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016This paper addresses the issue of direction-of-arrival (DOA) estimation with an objective to eliminate the off-grid effect of the sparsity-based methods and enlarge the maximum number of distinguishable signals in the subspace-based methods. We first reconstruct the covariance matrix of the array output in the Toeplitz structure and then employ the ...
Xiaohuan Wu, Wei-Ping Zhu, Jun Yan
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Robust Beamforming Based on Covariance Matrix Reconstruction and ADMM
2021 CIE International Conference on Radar (Radar), 2021Pengcheng Gong, Zhaobin Wang, Kaiyan Xu
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