Journal articles: 'Robust Capon Beamforming'

1

Stoica, P., Zhisong Wang, and Jian Li. "Robust Capon beamforming." IEEE Signal Processing Letters 10, no. 6 (June 2003): 172–75. http://dx.doi.org/10.1109/lsp.2003.811637.

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Li, Yang, Hong Ma, De Yu, and Li Cheng. "Iterative robust Capon beamforming." Signal Processing 118 (January 2016): 211–20. http://dx.doi.org/10.1016/j.sigpro.2015.07.004.

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Somasundaram, S. D. "Linearly Constrained Robust Capon Beamforming." IEEE Transactions on Signal Processing 60, no. 11 (November 2012): 5845–56. http://dx.doi.org/10.1109/tsp.2012.2212889.

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Hongqing, Liu, Liao Guisheng, and Zhang Jie. "A robust adaptive Capon beamforming." Signal Processing 86, no. 10 (October 2006): 2820–26. http://dx.doi.org/10.1016/j.sigpro.2005.11.010.

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Xie, Bin-bin, Lu Gan, and Li-ping Li. "Robust Capon Beamforming with Orthomodular Constraint." Journal of Electronics & Information Technology 33, no. 9 (September 30, 2011): 2045–49. http://dx.doi.org/10.3724/sp.j.1146.2011.00127.

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Kikuchi, S., H. Tsuji, and A. Sano. "Autocalibration Algorithm for Robust Capon Beamforming." IEEE Antennas and Wireless Propagation Letters 5 (2006): 251–55. http://dx.doi.org/10.1109/lawp.2006.874070.

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7

Song, Hai Yan, Jie Shi, Bo Sheng Liu, Ye Tian, and Jun Ye. "Robust Adaptive Beamforming Applied to Planar Array." Applied Mechanics and Materials 347-350 (August 2013): 3930–33. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3930.

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Capon beamforming has better resolution and interference rejection capability. However, its performance will seriously degrade due to noise, array steering vector error, and other factors. In this paper, a robust Capon beamforming applied to a planar array is described. It is shown that the proposed method is the natural extension of the original Vector Optimization Robust Beamforming algorithm to the case of a planar array, and can be reformulated as a convex second-order cone program and solved by SEDUMI. Computer simulation has shown that the proposed method has better performance than other conventional methods, such as narrower main lobe and lower side lobe.

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Li, Shu Ming, and Xian Feng Liu. "Robust Adaptive Beamforming Imaging Approach for Stepped-Frequency through-the-Wall Radar." Applied Mechanics and Materials 130-134 (October 2011): 45–49. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.45.

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A robust Capon adaptive beamforming imaging method was proposed for stepped-frequency through-the-wall radar. According to the ideal of robust Capon beamforming (RCB) under the steering vector uncertainty set, Capon optimal weight vector in frequcency domain is obtained by accurately estimating Compensation function vector. Then signal energy is estimated by freqency weighting, and coherence factor (CF) weighting is applied to suppress sidelobe level. Therefore, this method not only achieves higher resolution and much better interference suppression capabilities, but also improves imaging contrast.

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Xi, Zaifang, Xiao-feng Wu, Shuyue Wu, Zhijun Tang, and Shigang Hu. "Low-Complexity Robust Capon Beamforming Based on Reduced-Rank Technique." Journal of Electrical and Computer Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/583075.

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Existing robust Capon beamformers achieve robustness against steering vector errors at a high cost in terms of computational complexity. Computationally efficient robust Capon beamforming approach based on the reduced-rank technique is proposed in this paper. The proposed method projects the received data snapshots onto a lower dimensional subspace consisting of the matched filters of the multistage Wiener filter (MSWF). The subsequent adaptive beamforming will then be performed within this subspace. The combination of the benefit of the robust adaptive beamforming and the reduced-rank technique improves the performance on combating steering vector errors and lowering the computational complexity.

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10

Liao, Guisheng, Hongqing Liu, and Jun Li. "A Subspace-based Robust Adaptive Capon Beamforming." PIERS Online 2, no. 4 (2006): 374–79. http://dx.doi.org/10.2529/piers050906114239.

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11

Liu Zhen, Sun Chao, Liu Xiong-Hou, and Guo Qi-Li. "Robust Capon beamforming with weighted sparse constraint." Acta Physica Sinica 65, no. 10 (2016): 104303. http://dx.doi.org/10.7498/aps.65.104303.

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CHU, Yi. "Robust Capon Beamforming Using Pseudo-Interference Techniques." IEICE Transactions on Communications E93-B, no. 5 (2010): 1326–29. http://dx.doi.org/10.1587/transcom.e93.b.1326.

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13

Somasundaram, Samuel D. "Wideband Robust Capon Beamforming for Passive Sonar." IEEE Journal of Oceanic Engineering 38, no. 2 (April 2013): 308–22. http://dx.doi.org/10.1109/joe.2012.2223560.

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14

Jian Li, P. Stoica, and Zhisong Wang. "On robust capon beamforming and diagonal loading." IEEE Transactions on Signal Processing 51, no. 7 (July 2003): 1702–15. http://dx.doi.org/10.1109/tsp.2003.812831.

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15

Zhang, Wei, Ju Wang, and Siliang Wu. "Robust Capon beamforming against large DOA mismatch." Signal Processing 93, no. 4 (April 2013): 804–10. http://dx.doi.org/10.1016/j.sigpro.2012.10.002.

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16

Somasundaram, Samuel D., and Nigel H. Parsons. "Evaluation of Robust Capon Beamforming for Passive Sonar." IEEE Journal of Oceanic Engineering 36, no. 4 (October 2011): 686–95. http://dx.doi.org/10.1109/joe.2011.2167374.

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17

Byrne, Dallan, Martin O'Halloran, Edward Jones, and Martin Glavin. "TRANSMITTER-GROUPING ROBUST CAPON BEAMFORMING FOR BREAST CANCER DETECTION." Progress In Electromagnetics Research 108 (2010): 401–16. http://dx.doi.org/10.2528/pier10090205.

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18

Somasundaram, Samuel D., Nigel H. Parsons, Peng Li, and Rodrigo C. de Lamare. "Reduced-dimension robust capon beamforming using Krylov-subspace techniques." IEEE Transactions on Aerospace and Electronic Systems 51, no. 1 (January 2015): 270–89. http://dx.doi.org/10.1109/taes.2014.130485.

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19

Zhang, W., S. Wu, and J. Wang. "Robust Capon beamforming in presence of large DOA mismatch." Electronics Letters 49, no. 1 (January 2013): 75–76. http://dx.doi.org/10.1049/el.2012.3182.

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20

Hao, Yu, Nan Zou, and Guolong Liang. "Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar." Journal of Marine Science and Engineering 7, no. 3 (March 26, 2019): 80. http://dx.doi.org/10.3390/jmse7030080.

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Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

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21

Wen, Fei, Qun Wan, Hewen Wei, Rong Fan, and Yongjie Luo. "Robust Capon beamforming exploiting the second-order noncircularity of signals." Signal Processing 102 (September 2014): 100–111. http://dx.doi.org/10.1016/j.sigpro.2014.03.002.

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22

XU, LI, XIA XIAO, and TAKAMARO KIKKAWA. "IMPROVED BEAMFORMING ALGORITHM FOR IMAGING RECONSTRUCTION FOR EARLY BREAST CANCER DETECTION BY UWB." Journal of Circuits, Systems and Computers 22, no. 10 (December 2013): 1340027. http://dx.doi.org/10.1142/s0218126613400276.

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Ultra-wide band (UWB) microwave imaging is a promising method for the breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal organisms. In this paper, a two-dimensional model of the breast organisms is numerically carried by the finite difference time domain (FDTD) method. The dispersion characteristics of the breast media are taken into account by single pole Debye model to approach the actual properties of the breast organism. In this method, a tumor is assumed in the model with two cases. The standard Capon beamforming (SCB) and doubly constrained robust Capon beamforming (DCRCB) algorithm performed to reconstruct the image is described in detail. The tumor can be detected and localized using the proposed algorithm and the result demonstrates a good stability of DCRCB algorithm.

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23

Somasundaram, S. D. "Reduced dimension robust Capon beamforming for large aperture passive sonar arrays." IET Radar, Sonar & Navigation 5, no. 7 (2011): 707. http://dx.doi.org/10.1049/iet-rsn.2010.0351.

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24

Bi, Yang, Xi’an Feng, and Yangmei Zhang. "Optimized Sonar Broadband Focused Beamforming Algorithm." Algorithms 12, no. 2 (February 5, 2019): 33. http://dx.doi.org/10.3390/a12020033.

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Biases of initial direction estimation and focusing frequency selection affect the final focusing effect and may even cause algorithm failure in determining the focusing matrix in the coherent signal–subspace method. An optimized sonar broadband focused beamforming algorithm is proposed to address these defects. Initially, the robust Capon beamforming algorithm was used to correct the focusing matrix, and the broadband signals were then focused on the optimal focusing frequency by the corrected focusing matrix such that the wideband beamforming was transformed into a narrowband problem. Finally, the focused narrowband signals were beamformed by the second-order cone programming algorithm. Computer simulation results and water pool experiments verified that the proposed algorithm provides a good performance.

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25

Xia, Dong. "Robust STAP Algorithm under the Steering Vector Uncertainty Set." Advanced Materials Research 403-408 (November 2011): 2640–44. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2640.

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For solving the problem of optimum space-time adaptive processing (STAP) under the steering vector mismatch, a robust STAP algorithm is proposed based on the concept of robust Capon beamforming. The model of the steering vector error is established, describing the uncertainty of the desired steering vector. And a new optimization criterion is formed, by which the robust weight vector is acquired. Eventually experimental results and analysis are given with simulation signals. It is verified that the presented method is insensitive to the steering vector error and has a bigger improvement factor.

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26

Zhang, Tao, and Liguo Sun. "Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels." International Scholarly Research Notices 2014 (November 3, 2014): 1–8. http://dx.doi.org/10.1155/2014/260875.

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The performance of the conventional adaptive beamformer is sensitive to the array steering vector (ASV) mismatch. And the output signal-to interference and noise ratio (SINR) suffers deterioration, especially in the presence of large direction of arrival (DOA) error. To improve the robustness of traditional approach, we propose a new approach to iteratively search the ASV of the desired signal based on the robust capon beamformer (RCB) with adaptively updated uncertainty levels, which are derived in the form of quadratically constrained quadratic programming (QCQP) problem based on the subspace projection theory. The estimated levels in this iterative beamformer present the trend of decreasing. Additionally, other array imperfections also degrade the performance of beamformer in practice. To cover several kinds of mismatches together, the adaptive flat ellipsoid models are introduced in our method as tight as possible. In the simulations, our beamformer is compared with other methods and its excellent performance is demonstrated via the numerical examples.

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27

Liu, Yulong, Yingzeng Yin, Ruilong Li, and Ru Fang. "Flexible Null Broadening Robust Beamforming Based on JADE." Applied Sciences 12, no. 18 (September 17, 2022): 9329. http://dx.doi.org/10.3390/app12189329.

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In order to flexibly and completely suppress dynamic interference, a flexible and robust beamforming based on JADE is proposed in this paper. In addition, it is insensitive to the gain–phase errors of the array. Firstly, the actual steering vector with gain–phase errors is separated from the received snapshot data by the joint approximate diagonalization of eigenmatrix (JADE) algorithm. Secondly, the direction of arrival (DOA) of interference can be estimated from the separated actual steering vector by the correlation coefficient method. Thus, the actual interference steering vector with gain–phase errors can be selected by the correlation coefficient with the nominal steering vector constructed by the estimated DOA. Then, the interference covariance matrix can be reconstructed by the actual interference steering vector, and the interference power estimated by the Capon power spectral. Finally, according to the prior information of the interference, only the dynamic interference covariance matrix is tapered by the novel covariance matrix trap (CMT), which can flexibly broaden and deepen the null. Simulation results show that the depth of the proposed beamformer is more than 10 dB deeper than that of the traditional algorithm in the non-stationary interference. In addition, it can save at least 2 degrees of freedom compared to the traditional method.

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28

Xiao Xia, Song Hang, Wang Liang, Wang Zong-Jie, and Lu Hong. "Ultra-wideband microwave robust Capon beamforming imaging system for early breast cancer detection." Acta Physica Sinica 63, no. 19 (2014): 194102. http://dx.doi.org/10.7498/aps.63.194102.

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29

Hossain, Md Delwar, and Ananda Sanagavarapu Mohan. "Eigenspace Time-Reversal Robust Capon Beamforming for Target Localization in Continuous Random Media." IEEE Antennas and Wireless Propagation Letters 16 (2017): 1605–8. http://dx.doi.org/10.1109/lawp.2017.2653809.

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30

Liu, Yipeng, and Qun Wan. "Sidelobe Suppression for Robust Capon Beamforming With Mainlobe-to-Sidelobe Power Ratio Maximization." IEEE Antennas and Wireless Propagation Letters 11 (2012): 1218–21. http://dx.doi.org/10.1109/lawp.2012.2223451.

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31

Coviello, Christian M., Stuart R. Faragher, and Constantin‐C Coussios. "Robust Capon beamforming for passive cavitation mapping during high‐intensity focused ultrasound therapy." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2280. http://dx.doi.org/10.1121/1.3507982.

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32

Xiao, Xia, Li Xu, and Qin-Wei Li. "A double constrained robust capon beamforming based imaging method for early breast cancer detection." Chinese Physics B 22, no. 9 (September 2013): 094101. http://dx.doi.org/10.1088/1674-1056/22/9/094101.

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33

Mohammadzadeh, Saeed, and Osman Kukrer. "Modified Robust Capon Beamforming with Approximate Orthogonal Projection onto the Signal-Plus-Interference Subspace." Circuits, Systems, and Signal Processing 37, no. 12 (April 25, 2018): 5351–68. http://dx.doi.org/10.1007/s00034-018-0818-4.

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34

WANG, Jie, Guangquan YANG, Yi HU, and Chunliang ZHANG. "A Two-Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming." Chinese Journal of Electronics 28, no. 5 (September 1, 2019): 962–67. http://dx.doi.org/10.1049/cje.2019.06.016.

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35

Li, Wenxing, Xiaojun Mao, Zhuqun Zhai, and Yingsong Li. "High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3743509.

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A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.

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36

Hou, Yuguan, Hefu Gao, Qinghong Huang, Jinzi Qi, Xingpeng Mao, and Cunfeng Gu. "A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing." IEEE Access 7 (2019): 80478–90. http://dx.doi.org/10.1109/access.2019.2923065.

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37

Li, Hongtao, Zhoupeng Ding, Sirui Tian, and Songpo Jin. "Robust Adaptive Transmit Beamforming under the Constraint of Low Peak-to-Average Ratio." Sensors 22, no. 19 (September 26, 2022): 7278. http://dx.doi.org/10.3390/s22197278.

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In radar detection, in order to make the beam have variable directivity, a Capon beamformer is usually used. Although this traditional beamformer enjoys both high resolution and good interference suppression, it usually leads to high sidelobe and is sensitive to array steering vector (ASV) mismatch. To overcome such problems, this study devises a novel, robust adaptive beamformer that is robust to ASV mismatch under the constraint where the sidelobe is oriented to the ground. Moreover, to make full use of the transmit power, the constraint of a low peak-to-average power ratio (PAPR) is also taken into consideration. Accordingly, this robust adaptive beamformer is developed by optimizing a transmitting beamformer constrained by ASV mismatch and low PAPR. This optimization problem is transformed into a second-order cone programming (SOCP) problem which can be efficiently and exactly solved. The proposed transmit beamformer possesses not only adaptive interference rejection ability and robustness against ASV mismatch, but also direct sidelobe control and a low PAPR. Simulation results are presented to demonstrate the superiority of the proposed approach. The proposed method can make the peak sidelobe level (PSL) level on the ground side below −30 dB.

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38

Lv, Yan, Fei Cao, Chuan He, Xiaowei Feng, and Jianqiang Qin. "Covariance Matrix Reconstruction via a Subspace Method and Spatial Spectral Estimation for Robust Adaptive Beamforming." Wireless Communications and Mobile Computing 2022 (May 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/9152752.

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Several robust adaptive beamforming (RAB) algorithms based on interference plus noise covariance matrix (INCM) reconstruction have been recently proposed, which can enhance the robustness of beamforming algorithms when certain mismatches occur in the model. However, some approaches ignore the resolution of the Capon spectral estimator (CSE), leading to reconstruction errors. This paper proposes a novel RAB algorithm formulated using the subspace projection method and spatial spectral estimation (SSE), which is named INCM-SSE. First, without using the CSE, the subspace projection matrix (SPM) is obtained through the integral of the angular sector where the signal of interest (SOI) is located. Subsequently, after estimating the direction of arrival (DOA) of incident signals using the multiple signal classification (MUSIC) algorithm, we project the sample covariance matrix (SCM) onto the SPM to eliminate the SOI influence. Then, the estimation method of interference powers is derived. Moreover, the INCM is reconstructed based on the estimated powers and steering vector (SV) of interferences. The SV of the SOI is optimized by solving a quadratic convex optimization problem. The INCM-SSE algorithm not only employs SSE to improve the angular resolution but also reduces the influence of the SOI component by using SPM. Simulation results indicate that the proposed method is robust against various types of mismatches, thus achieving superior overall performance.

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39

Liu, Yibin, Chunyang Wang, Jian Gong, Ming Tan, and Geng Chen. "Robust Suppression of Deceptive Jamming with VHF-FDA-MIMO Radar under Multipath Effects." Remote Sensing 14, no. 4 (February 15, 2022): 942. http://dx.doi.org/10.3390/rs14040942.

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Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect.

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40

Thakur, Diksha, Vikas Baghel, and Salman Raju Talluri. "Proximal gradient method based robust Capon beamforming against large DOA mismatch." Frequenz, April 23, 2021. http://dx.doi.org/10.1515/freq-2020-0133.

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Abstract The Capon beamformer has excellent resolution and interference suppression capability but due to various attributes of practical environment such as inaccurate and/or insufficient information about the source, transmission medium and antenna array its performance deteriorates. To enhance its performance various efforts have been devoted and one effective method is presented here. In this paper, a novel and efficient robust Capon beamformer is devised which is based on proximal gradient method (PGRCB) and the robustness is achieved through remodeling the optimization problem of standard Capon beamformer (SCB). In the proposed PGRCB, the proximal gradient method is used to formulate a new optimization problem in order to obtain the optimum weights of the robust beamformer. The proposed method can achieve better performance as compared to some recent methods in the literature and its effectiveness is verified by the simulation results.

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Journal articles on the topic 'Robust Capon Beamforming'

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