Range-coherent matched field processing for low signal-to-noise ratio localization

Akins, Franklin H.; Kuperman, W. A.

doi:10.1121/10.0005586

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…The Bartlett processor is robust in mismatched environmental param condition, but it produces more noticeable sidelobes in the ambiguity surface. MVDR effectively reduces these sidelobes, although localization errors may increase if the acoustic fields environmental param mismatched [5] .…”

Section: Introductionmentioning

confidence: 99%

Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

Cui,

Shen,

Yang

2024

J. Phys.: Conf. Ser.

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In the deep-sea direct arrival region, a significant multipath characteristic is presented by the propagation of acoustic signals. This characteristic is closely linked to the distance and depth of the sound source and can be utilized for source localization. In this study, the changes in incidence angle characteristics of multipath signals at different distances and depths are analysed initially. Subsequently, the high-resolution azimuthal spectra are obtained using the Sparse Bayesian Learning (SBL) method. The azimuthal spectra are then matched with multiple incidence angles using the Gaussian kernel function, facilitating the localization of sound sources. Throughout this paper, the impacts of different distance, depth, and SNR conditions on the model are assessed through simulations. Furthermore, the model’s validity is confirmed by utilizing experimental data from an explosion sound source.

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Section: Introductionmentioning

confidence: 99%

Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

Cui,

Shen,

Yang

2024

J. Phys.: Conf. Ser.

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“…Passive source localization is a challenging issue in shallow waters. Classical matchedfield processing (MFP) matches received array data with a dictionary of replica vectors under comprehensive a priori environmental knowledge [14,15]. It is computationally expensive and highly susceptible to environmental uncertainty when the knowledge of the ocean environment is not adequate.…”

Section: Introductionmentioning

confidence: 99%

Striation-Based Beamforming with Two-Dimensional Filtering for Suppressing Tonal Interference

Li,

Wu,

Guo

et al. 2023

JMSE

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Based on the interference spectrogram in the element–frequency domain using the data measured by the horizontal linear array, the source range can be estimated through the striation-based beamforming (SBF) method and its variants. Estimation of the striation slope is the basis for these ranging methods. But in practical scenarios, the tonal interferences and other noise make it difficult to estimate the slope. In this paper, we proposed a two-dimensional low-pass filtering method after the two-dimensional discrete Fourier transform (2D-DFT) of the element–frequency domain spectrogram. The signals can be separated from the interference and noise using this filter. For the linear frequency-modulated signal without a known waveform, we also proposed an extraction and phase compensation method based on the time–frequency spectrogram, and the acoustic data obtained can be used for source ranging. The experimental results indicate that the methods proposed are feasible.

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“…For underwater localization, matched field processing (MFP) is an effective technique and has been widely investigated [18][19][20][21]. MFP exploits an acoustic model to calculate the field replica, from an acoustic source, to match the field measured by an array of hydrophones [22,23]. A measure of the match defines an ambiguity function (AF) computed on a grid of points in space (range and depth) covering the area of interest.…”

Section: Introductionmentioning

confidence: 99%

Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers

Shen

Henson

et al. 2022

Sensors

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For underwater acoustic (UWA) communication in sensor networks, the sensing information can only be interpreted meaningfully when the location of the sensor node is known. However, node localization is a challenging problem. Global Navigation Satellite Systems (GNSS) used in terrestrial applications do not work underwater. In this paper, we propose and investigate techniques based on matched field processing for localization of a single-antenna UWA communication receiver relative to one or more transmit antennas. Firstly, we demonstrate that a non-coherent ambiguity function (AF) allows significant improvement in the localization performance compared to the coherent AF previously used for this purpose, especially at high frequencies typically used in communication systems. Secondly, we propose a two-step (coarse-to-fine) localization technique. The second step provides a refined spatial sampling of the AF in the vicinity of its maximum found on the coarse space grid covering an area of interest (in range and depth), computed at the first step. This technique allows high localization accuracy and reduction in complexity and memory storage, compared to single step localization. Thirdly, we propose a joint refinement of the AF around several maxima to reduce outliers. Numerical experiments are run for validation of the proposed techniques.

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Range-coherent matched field processing for low signal-to-noise ratio localization

Cited by 11 publications

References 38 publications

Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

Striation-Based Beamforming with Two-Dimensional Filtering for Suppressing Tonal Interference

Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers

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