Grid-free compressive mode extraction

Park, Yongsung; Gerstoft, Peter; Seong, Woojae

doi:10.1121/1.5094345

Cited by 23 publications

(7 citation statements)

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“…Solve the least squares solution of the Langrage multiplier through methods previously published [21,22]. The least square solution of s is:s=(K−λminI)−1F…”

Section: Hand–eye Calibration Methodsmentioning

confidence: 99%

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

Wang

2019

Sensors

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To improve the accuracy and robustness of hand–eye calibration, a hand–eye calibration method based on Schur matric decomposition is proposed in this paper. The accuracy of these methods strongly depends on the quality of observation data. Therefore, preprocessing observation data is essential. As with traditional two-step hand–eye calibration methods, we first solve the rotation parameters and then the translation vector can be immediately determined. A general solution was obtained from one observation through Schur matric decomposition and then the degrees of freedom were decreased from three to two. Observation data preprocessing is one of the basic unresolved problems with hand–eye calibration methods. A discriminant equation to delete outliers was deduced based on Schur matric decomposition. Finally, the basic problem of observation data preprocessing was solved using outlier detection, which significantly improved robustness. The proposed method was validated by both simulations and experiments. The results show that the prediction error of rotation and translation was 0.06 arcmin and 1.01 mm respectively, and the proposed method performed much better in outlier detection. A minimal configuration for the unique solution was proven from a new perspective.

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“…Solve the least squares solution of the Langrage multiplier through methods previously published [21,22]. The least square solution of s is:s=(K−λminI)−1F…”

Section: Hand–eye Calibration Methodsmentioning

confidence: 99%

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

Wang

2019

Sensors

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“…Although the underlying theory is complex, offthe-shelf toolboxes such as the CVX software (ConVeX: a library for convex optimization) [35] are available to solve the problem. This approach was notably used to estimate wavenumbers (and modal depth functions) considering a VLA and a source at multiple ranges [20]. However, the main drawback of this procedure is its high computational cost.…”

Section: Off-grid Cs Algorithmmentioning

confidence: 99%

“…They have been notably applied to underwater acoustic problems, such as plane wave beamforming [19] or modal estimation using a vertical line array (VLA) and a source at multiple ranges [20]. Later, May 27, 2021 DRAFT Paviet-Salomon et al [21] proposed to use grid-free CS to perform modal wavenumber estimation using a single broadband source and a small number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Frequency-Wavenumber Diagrams Using a Physics-Based Grid-Free Compressed Sensing Method

Paviet-Salomon

Bonnel

Dorffer

et al. 2022

IEEE J. Oceanic Eng.

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Shallow water propagation can be described using modal theory. For low frequency sources, propagated signals are composed of a few dispersive modes, each of them propagating with its own frequencydependent wavenumber. Modal estimation, and particularly wavenumber estimation, is of great interest in seabed characterization but classically requires a large and dense horizontal line array (HLA). The compressed sensing (CS) paradigm, which allows one to reduce the number of sensors, has been used to overcome this limitation. However, CS performance is directly linked to the discrete basis used in the process and is known to degrade with basis mismatch. To mitigate this issue, the current paper proposes a physics-based grid-free approach to perform wavenumber estimation using a HLA with a limited number of sensors and a single broadband source. The proposed method has three main features: it starts with a speed correction to prevent wavenumber aliasing (using water sound speed at the array location), it then embeds physical prior (the modal dispersion relation) at the core of the CS framework, followed by a CS grid-free approach. The performance of the method is quantified on simulated data using the Jaccard's distance. The method is then applied successfully on experimental data from the 2017 Seabed Characterization Experiment.

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“…The solution of the sparse reconstruction problem can then be obtained by using the CVX software [15]. This is the approach considered in [16] for the problem of wavenumber estimation with vertical line arrays. A main drawback of such procedure is its high complexity and consequently computational time.…”

Section: Grid-free Compressed Sensingmentioning

confidence: 99%

Dispersive Grid-free Orthogonal Matching Pursuit for Modal Estimation in Ocean Acoustics

Paviet-Salomon

Dorffer

Bonnel

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Considering low-frequency acoustic sources, shallowwater environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.

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Grid-free compressive mode extraction

Cited by 23 publications

References 50 publications

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

Estimation of Frequency-Wavenumber Diagrams Using a Physics-Based Grid-Free Compressed Sensing Method

Dispersive Grid-free Orthogonal Matching Pursuit for Modal Estimation in Ocean Acoustics

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