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Relevant bibliographies by topics / Adaptive snapshots

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Journal articles

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 wit

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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 e

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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 fiv

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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

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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 hyper

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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

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