Robust beamforming for wideband array based on spectrum subspaces

Xie, Zhuang; Fan, Chongyi; Zhu, Jiahua; Huang, Xiaotao

doi:10.1049/iet-rsn.2019.0598

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…This behavior makes a Rotman lens a true-time delay device with varying phase fronts at the array input via providing path delays within the lens cavity [ 14 ]. This path delay procedure is independent of any frequency [ 15 ], making the lens the best choice for various wideband applications that require wide-angle frequency scanning. In the lens design, conduction and absorption losses can be reduced by further adding dummy ports.…”

Section: Rotman Lensmentioning

confidence: 99%

mmW Rotman Lens-Based Sensing: An Investigation Study

Sethi

Ibrahim

Issa

et al. 2021

Sensors

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A Rotman lens is a wideband true-time delay device. Due to its simplistic structure with wave/signal routing capabilities, it has been widely utilized as a beamforming device in numerous communication systems. Since the basic Rotman lens design incorporates multiple input, output, and dummy ports, in this study, and for the first time, we utilized a Rotman lens as a sensor. The main idea was to gather abundant information from available Rotman lens ports to obtain better sensing performance. The realized lens is optimized to work in the millimeter wave (mmW) band from 27 to 29 GHz with a focus on a central frequency of 28 GHz. The design has a footprint of 140 × 103 × 0.8 mm3. The polarity correlator was used to characterize the material under investigation.

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Section: Rotman Lensmentioning

confidence: 99%

mmW Rotman Lens-Based Sensing: An Investigation Study

Sethi

Ibrahim

Issa

et al. 2021

Sensors

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“…Capon beamforming technology is often used in passive sonar arrays to improve the detection performance of weak target signals and suppress interference [1]. However, the performance of Capon beamformer will be seriously degraded in practical applications due to the influence of array element amplitude and phase errors, array element position floating and other problems [2].Aiming at the problems arising from the above practical application, many robust adaptive beamforming algorithms have been proposed by scholars, such as loading algorithms [3], weight norm constraint algorithms [4], covariance matrix reconstruction algorithms [5][6][7], etc. However, the nulling formed by the above method in the interference direction is very narrow.…”

Section: Introductionmentioning

confidence: 99%

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Wang

Zhang

2023

Preprint

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Beamforming technology is very important for passive sonar to detect targets. However, the performance of beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on weighted similarity vector is proposed for underwater fast moving strong interference signals. First, The covariance matrix is reconstructed through the correlation between the steering vector and the subspace eigenvector, which is used to calculate the similarity vector. Then, the maximum power in the interference angle sector is used as the virtual interference source power to broaden the null trap in the angle sector. Next, the difference between the optimal weight vector and the similar vector is minimized, interference-plus-noise power constraints and norm constraints are added, and it is written as a quadratic constrained quadratic programming (QCQP) problem, which is converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution is calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has good suppression effect and strong robustness for fast strong interference.

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“…Simulation results showed that this algorithm had good performance for different types and ranges of array manifold deviations. Furthermore, the wideband interference will be further considered in future work [10]. Zou et al developed a hybrid analytical-numerical method that combines the analytical technique with the acoustic superposition approach to predict the sound radiation of a spherical double-shell within the ocean's acoustic environment.…”

Section: Introductionmentioning

confidence: 99%

Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference

Wang

et al. 2023

Remote Sensing

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Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential.

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Robust beamforming for wideband array based on spectrum subspaces

Cited by 4 publications

References 29 publications

mmW Rotman Lens-Based Sensing: An Investigation Study

mmW Rotman Lens-Based Sensing: An Investigation Study

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference

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