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Academic literature on the topic 'Adaptive snapshots'

Author: Grafiati
Published: 29 August 2023

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Journal articles on the topic "Adaptive snapshots"

1

YU, Jing, and Yaan LI. "Adaptive Beamforming with Inadequate Snapshots." Journal of Physics: Conference Series 787 (January 2017): 012025. http://dx.doi.org/10.1088/1742-6596/787/1/012025.

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2

Liao, Zhipeng, Keqing Duan, Jinjun He, Zizhou Qiu, and Binbin Li. "Robust Adaptive Beamforming Based on a Convolutional Neural Network." Electronics 12, no. 12 (2023): 2751. http://dx.doi.org/10.3390/electronics12122751.

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To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage convolutional neural network. The first stage includes convolutional blocks and residual blocks without downsampling; the blocks assess the covariance matrix precisely using finite snapshots. The second stage maps the first stage’s output to an adaptive weight vector employing a similar structure to the first stage. The two stages are pre-trained with different datasets and fine-tuned as end-to-end networks, simplifying the network training process. The two-stage structure enables the network to possess practical physical meaning, allowing for satisfying performance even with a few snapshots in the presence of array G/P errors. We demonstrate the resulting beamformer’s performance with numerical examples and compare it to various other adaptive beamformers.
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3

Wu, Xun, Jie Luo, Guowei Li, Shurui Zhang, and Weixing Sheng. "Fast Wideband Beamforming Using Convolutional Neural Network." Remote Sensing 15, no. 3 (2023): 712. http://dx.doi.org/10.3390/rs15030712.

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With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.
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4

Gong, C., L. Huang, D. Xu, and Z. Ye. "Knowledge‐aided robust adaptive beamforming with small snapshots." Electronics Letters 49, no. 20 (2013): 1258–59. http://dx.doi.org/10.1049/el.2013.2198.

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5

Brooker, D. J., Kay L. Gemba, and Laurie T. Fialkowski. "Overcoming snapshot-deficient measurements with knowledge-aided approaches." JASA Express Letters 2, no. 5 (2022): 054804. http://dx.doi.org/10.1121/10.0010455.

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The use of knowledge-aided covariance is considered for processing underwater acoustic array data in snapshot-deficient scenarios. The knowledge-aided formalism is a technique that combines array data with a known covariance to produce an invertible estimate. For underwater acoustics, simulations of ambient noise provide the a priori covariance allowing degraded signals to be processed adaptively in situations where the sample covariance matrix is rank-deficient. The method is demonstrated for matched field processing using the 21 element array event S5 from the SWellEx-96 experiment. With five snapshots, the knowledge-aided approach significantly reduces localization ambiguity compared to the adaptive white noise gain constraint processor.
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6

Sun, Xu, and Ranwei Li. "Robust adaptive beamforming method for active sonar in single snapshot." MATEC Web of Conferences 283 (2019): 03006. http://dx.doi.org/10.1051/matecconf/201928303006.

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Forming narrow beams is a useful way for active sonar to anti-reverberation when it works in the shallow water. High-resolution adaptive beamforming with the performance of narrow beamwidths and low sidelobe levels is a better and more efficient method, particularly in the scenario where the installation space for sonar array is limited, such as hull-mounted sonar. Due to the short duration of target echo signal in the complex and varying acoustic channel, conventional adaptive beamforming methods are invalid. Therefore, this paper proposes a robust adaptive beamforming method for active sonar in single snapshot, also called the steered dominant mode rejection (STDMR). Firstly, STDMR steered the sample covariance matrix (STCM) based on wide-band focusing, which the needed number of snapshots is greatly reduced. Secondly, by partial eigendecomposition, the large eigenvalues of the STCM which are greater than the noise energy and their eigenvectors are used for dominant mode rejection (DMR). DMR is a typical eigenspace-based algorithm which has small computational load and fast convergence speed. Finally, modified with the methods of diagonal loading of 3-5dB over the noise energy and signal mismatch protection, improved the robustness of this method. Simulation and experimental data analysis shows that the STDMR method achieves narrow beams and low-level sidelobes in single snapshot. Hence, the STDMR beamformer is an appropriate implementation to use for active sonar detection systems.
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7

Wang, He, Ting Zhang, Lei Cheng, and Hangfang Zhao. "Snapshot-deficient active target localization in beam-time domain using multi-frequency expectation-maximization algorithm." Journal of the Acoustical Society of America 153, no. 2 (2023): 990–1003. http://dx.doi.org/10.1121/10.0017164.

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The two-dimensional (2D) active target localization is generally hindered by the high temporal and spatial sidelobe levels in snapshot-deficient scenarios, where the adaptive approaches undergo performance degeneration since they require many snapshots to build the sample covariance matrix. Aiming at working robustly in snapshot-deficient active scenarios, a 2D expectation-maximization-based vertical-time-record (EMVTR) approach is proposed to compensate for the snapshot deficiency and achieve the high-resolution active localization by reconstructing the covariance matrix using estimated hyperparameters, i.e., signal powers and noise variance. With the short-time Fourier transform, the proposed approach could reduce echoes' temporal correlation and attain robust beam-time localization in mild reverberation. The multi-frequency EMVTR is derived from the single-frequency case to improve the weak echo localization. The performance is evaluated by considering single and multiple target echoes in simulation and a single moving target with tank experimental data. The results manifest the proposed EMVTR's robustness and effectiveness for the 2D active localization in snapshot-deficient scenarios.
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8

Ullmann, Sebastian, Marko Rotkvic, and Jens Lang. "POD-Galerkin reduced-order modeling with adaptive finite element snapshots." Journal of Computational Physics 325 (November 2016): 244–58. http://dx.doi.org/10.1016/j.jcp.2016.08.018.

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9

Li, Hongtao, Ke Wang, Chaoyu Wang, Yapeng He, and Xiaohua Zhu. "Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint." International Journal of Antennas and Propagation 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/765385.

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A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.
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10

Cui, Weichen, Tong Wang, Degen Wang, and Kun Liu. "An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior." Remote Sensing 14, no. 15 (2022): 3520. http://dx.doi.org/10.3390/rs14153520.

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Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better reconstruction of the clutter plus noise covariance matrix (CNCM). However, similar to other SBL-based algorithms, a large degree of freedom will bring a heavy burden to the real-time processing system. To overcome this drawback, an efficient localized reduced-dimension sparse recovery-based space-time adaptive processing (LRDSR-STAP) framework is proposed in this paper. By using a set of deeply weighted Doppler filters and exploiting prior knowledge of the clutter ridge, a novel localized reduced-dimension dictionary is constructed, and the computational load can be considerably reduced. Numerical experiments validate that the proposed method achieves better performance with significantly reduced computational complexity in limited snapshots scenarios. It can be found that the proposed LRDSR-CALM-STAP algorithm has the potential to be implemented in practical real-time processing systems.
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