Gridless three-dimensional compressive beamforming with the Sliding Frank-Wolfe algorithm

Chardon, Gilles; Boureau, Ulysse

doi:10.1121/10.0006790

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…The regularization parameter λ can be tuned to find the number of sources. In this work, we assume the number of trajectories to be known; thus, we set λ = 0 and develop greedy iterative algorithms 47 . From the solution ∆ * , we obtain estimates for the trajectory parameters W and their corresponding amplitudes using (13).…”

Section: A Beurling Lassomentioning

confidence: 99%

“…Gridless localization has been formulated as an atomic norm minimization (ANM) problem and solved using semi-definite programming in 1D and 2D scenarios [32][33][34][35][36][37][38][39][40][41] . Additionally, gridless methods have been applied for nonuniform arrays and wideband processing [42][43][44][45][46][47] . The Newtonized OMP (NOMP) algorithm is a variation of OMP that employs Newton steps to refine source parameters in each iteration 48 .…”

Section: Introductionmentioning

confidence: 99%

“…An alternative gridless approach is the Sliding Frank-Wolfe (SFW) algorithm 49 , which solves the Beurling LASSO problem, i.e., a traditional LASSO in the continuum 50 . SFW has been extended to 3D acous-tic source localization in a grid-free setting 47 , and the choice of the regularization parameter is vital in obtaining accurate solutions.…”

Section: Introductionmentioning

confidence: 99%

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Parametric Models for Doa Trajectory Localization

Pandey

Nannuru

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most localization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that captures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and subsequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA trajectories for moving sources more accurately than traditional signal models and methods.

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Section: A Beurling Lassomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parametric Models for Doa Trajectory Localization

Pandey

Nannuru

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Classical beamforming methods, proved useful in practice, are based on “Fourier transform in direction domain” and can obtain high resolution on the promise that the input SNR is high enough or the datasheet is long enough. Among the beamforming methods, conventional beamforming (CBF) has lower resolution ratio but higher adaptability since CBF uses phase deviation without prior known number of sources, which is needed in high-resolution beamforming methods, such as Minimum Variance Distortion-less Response (MVDR) and Multiple Signal Classification (MUSIC) [ 2 , 3 ]. Recently, conversion methods of time-frequency are boosting, with better and faster performance to obtain frequency.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Miao

Sun

et al. 2022

Sensors

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Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

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“…The general classical beamforming method is based on Fourier transform in the directional domain, where the input signal-to-noise ratio condition must be satisfied to ensure high resolution. The conventional beamforming method (CBF) achieves better adaptability by exploiting the phase deviation without prior knowledge of the number of signal sources, such as minimum variance distortion-free response (MVDR) and multiple signal classification (MUSIC) [ 4 , 5 ]. Although there are many beamforming methods for long-distance signals, the conditions of a high signal-to-noise ratio generally cannot be met.…”

Section: Introductionmentioning

confidence: 99%

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network

Wang

Ren

et al. 2022

Sensors

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Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than −43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of −48 dB.

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Gridless three-dimensional compressive beamforming with the Sliding Frank-Wolfe algorithm

Cited by 13 publications

References 32 publications

Parametric Models for Doa Trajectory Localization

Parametric Models for Doa Trajectory Localization

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network

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