Matched Field Processing Based on Bayesian Estimation

Zhu, Gao; Wang, Yingmin; Wang, Qi

doi:10.3390/s20051374

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In practice, such knowledge is almost impossible to achieve; note that this is a common problem of the MFP approach. This problem can be partly solved by frequent real-time measurements of the SSP; in particular, this can be done using the MFP inversion techniques [33][34][35][36]. It would be interesting to implement such techniques based on the communication signals, thus reusing the available resources in the communication system.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers

Shen

Henson

et al. 2022

Sensors

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“…Other estimators of beampower exist, and their development is an active field of research (e.g., Capon, 1969;Schmidt, 1986;Cox et al, 1987;Cox, 2000;Gal et al, 2014;Zhu et al, 2020).…”

Section: Matched Field Processingmentioning

confidence: 99%

“…Both conditions are true for most real-world applications. This is one of the main reasons other beamformers are being developed (e.g., Capon, 1969;Schmidt, 1986;Cox et al, 1987;Cox, 2000;Gal et al, 2014;Zhu et al, 2020). In future work, exploring their applicability to and further developing them in the context of elastic waves propagating in real Earth structure, seems like a clear way forward.…”

Section: Multiple Sourcesmentioning

confidence: 99%

“…In the seismological context, MFP has been applied succesfully on local (Corciulo et al, 2012;Umlauft & Korn, 2019;Umlauft et al, 2021) and regional scale (Gal et al, 2018). Recent developments in MFP include the development of different beamformers (e.g., Zhu et al, 2020), improved estimation of travel times (Gal et al, 2018), or estimating synthetic wave fields empirically (Gibbons et al, 2017). MFP is an attractive strategy for source localisation of the ambient seismic field.…”

Section: Introductionmentioning

confidence: 99%

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Matched Field Processing accounting for complex Earth structure: method and review

Schippkus¹,

Hadziioannou²

2022

Preprint

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Matched Field Processing (MFP) is a technique to locate the source of a recorded wave field. It is the generalization of beamforming, allowing for curved wavefronts. In the standard approach to MFP, simple analytical Green's functions are used as synthetic wave fields that the recorded wave fields are matched against. We introduce an advancement of MFP by utilizing Green's functions computed numerically for real Earth structure as synthetic wave fields. This allows in principle to incorporate the full complexity of elastic wave propagation, and through that provide more precise estimates of the recorded wave field's origin. This approach also further emphasizes the deep connection between MFP and the recently introduced interferometry-based source localisation strategy for the ambient seismic field. We explore this connection further by demonstrating that both approaches are based on the same idea: both are measuring the (mis-)match of correlation wave fields. To demonstrate the applicability and potential of our approach, we present two real data examples, one for an earthquake in Southern California, and one for secondary microseism activity in the Northeastern Atlantic and Mediterranean Sea. Tutorial code is provided to make MFP more approachable for the broader seismological community.

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“…When the acoustic propagation model is accounted for in the passive sonar array signal processing, we refer to the procedure as matched-field processing (MFP) [9]- [12]. MFP is a well-established generalization of plane-wave beamforming for USL where the steering or replica vector is derived from a solution of the wave equation in the frequency domain (i.e., the Helmholtz equation) for a point source, especially common for shallow water applications where multipath propagation can yield useful information to infer the source range and depth.…”

Section: Introductionmentioning

confidence: 99%

Underwater Source Localization via Spectral Element Acoustic Field Estimation

Manduzio,

Forti,

Sabatini

et al. 2023

IEEE Sensors J.

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Underwater source localization from a passive array of acoustic sensors is a challenging problem, especially in complex environments characterized by multipath and reverberation effects, irregular seabed geometry, and low signal-to-noise ratio. This paper proposes a recursive Bayesian approach that propagates a spectral-element approximation of the wave equation to model the discretized space-time dynamics of the acoustic field conditioned on the position of the source, and sequentially estimates the field and the position of the radiating source from the acoustic measurements. We pursue a multiple-model approach where each model assumes either the source absence or its presence within a specific spectral element. To handle the high dimension of the large-scale field estimation problem and reduce the computational complexity, the multiple-model filter is implemented by using ensemble Kalman filters. Finally, the effectiveness of the proposed multiple-model spectral-element ensemble Kalman filter is demonstrated through simulation experiments in underwater acoustic environments with regular and irregular seabed geometry and via comparison with the standard matched-field processing method.

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Matched Field Processing Based on Bayesian Estimation

Cited by 9 publications

References 21 publications

Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers

Coarse-to-Fine Localization of Underwater Acoustic Communication Receivers

Matched Field Processing accounting for complex Earth structure: method and review

Underwater Source Localization via Spectral Element Acoustic Field Estimation

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