A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix

2012 ◽  
Vol 59 (4) ◽  
pp. 660-667 ◽  
Author(s):  
B. M. Asl ◽  
A. Mahloojifar
Keyword(s):  
Covariance Matrix ◽  
Ultrasound Imaging ◽  
Low Complexity ◽  
Adaptive Beamformer

scholarly journals Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3368
Author(s):  
Rui Hu ◽  
Jun Tong ◽  
Jiangtao Xi ◽  
Qinghua Guo ◽  
Yanguang Yu
Keyword(s):  
Covariance Matrix ◽  
Communication Systems ◽  
Low Complexity ◽  
Low Rank ◽  
Covariance Matrix Estimation ◽  
Hardware Complexity ◽  
Matrix Estimation ◽  
Planar Arrays ◽  
Rank Matrix ◽  
Low Rank Matrix

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as potential candidates for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.

scholarly journals Two-Dimensional Direction-of-Arrival Estimation of Noncircular Signals in Coprime Planar Array with High Degree of Freedom

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Haiyun Xu ◽  
Daming Wang ◽  
Size Lin ◽  
Bin Ba ◽  
Yankui Zhang
Keyword(s):  
Covariance Matrix ◽  
Direction Of Arrival ◽  
Low Complexity ◽  
Degree Of Freedom ◽  
Spectral Peak ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Planar Array ◽  
Noncircular Signals ◽  
Peak Search

In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, there are problems of the limited degree of freedom (DOF) and high complexity caused by the spectral peak search. We utilize the time-domain characteristics of signals and present a high DOF algorithm with low complexity based on the noncircular signals. The paper first analyzes the covariance matrix and ellipse covariance matrix of the received signals, vectorizes these matrices, and then constructs the received data of a virtual uniform rectangular array (URA). 2D spatial smoothing processing is applied to calculate the covariance of the virtual URA. Finally, the paper presents an algorithm using 2D multiple signal classification and an improved algorithm using unitary estimating signal parameters via rotational invariance techniques, where the latter solves the closed-form solutions of DOAs replacing the spectral peak search to reduce the complexity. The simulation experiments demonstrate that the proposed algorithms obtain the high DOF and enable to estimate the underdetermined signals. Furthermore, both two proposed algorithms can acquire the high accuracy.

scholarly journals Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7783
Author(s):  
Yanliang Duan ◽  
Xinhua Yu ◽  
Lirong Mei ◽  
Weiping Cao
Keyword(s):  
Covariance Matrix ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Training Sequence ◽  
Adaptive Beamforming ◽  
Projection Matrix ◽  
Robust Adaptive Beamforming ◽  
Sample Covariance ◽  
Blocking Matrix ◽  
Robust Adaptive

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

A Low-complexity Minimum Variance Algorithm Combined with Power Method for Ultrasound Imaging

2020 ◽  
Vol 66 (2) ◽  
pp. 204-212
Author(s):  
Ping Wang ◽  
Tingting Du ◽  
Linhong Wang ◽  
Lu Kong ◽  
Xitao Li ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Low Complexity ◽  
Minimum Variance ◽  
Power Method
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