Improving Sonar Performance in Shallow Water Using Adaptive Beamforming

Blomberg, Ann Elisabeth Albright; Austeng, Andreas; Hansen, Roy Edgar; Synnes, Stig Asle Vaksvik

doi:10.1109/joe.2012.2226643

Cited by 50 publications

(11 citation statements)

References 24 publications

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“…In navigable rivers, river bathymetry is typically measured with single beam or multi-beam sonar on-board manned or unmanned vessels (Bio et al, 2020;Halmai et al, 2020;Leyland et al, 2017;Specht et al, 2020;Stateczny et al, 2019;Young et al, 2017). However, sonar systems have limitations in measuring very shallow depths due to surface clutter and multipath effects (Albright Blomberg et al, 2013); furthermore, the accuracy of sonar signi cantly degrades in vegetated rivers (Helminen et al, 2019), indeed the high level of re ection of sound waves from the vegetation can result in depth measurements within vegetation canopy (Sabol, 2002). Additionally, deployment of boats can be time-consuming in remote areas and is limited to navigable water.…”

Section: Sonarmentioning

confidence: 99%

Mapping inland water bathymetry with Ground Penetrating Radar (GPR) on board Unmanned Aerial Systems (UASs)

Bandini

Kooij

Mortensen

et al. 2021

Preprint

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Bathymetry of inland waterbodies is essential for river maintenance and flood risk management. Traditionally, in shallow depths bathymetry is retrieved by operators wading through the waterbody with Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS), whilst in deeper depths it is retrieved with sonar instruments on manned or unmanned. In the past, researchers have documented the use of Ground Penetrating Radar (GPR) on boats (i.e. water-coupled GPR) for monitoring the bathymetry of frozen and non-frozen waterbodies. Furthermore, GPR has been used on helicopters for monitoring ice and snow thickness. However, deployment of GPR on board Unmanned Aerial Systems (UASs) in non-frozen inland water bodies with electric conductivity higher than 100 µS/cm (as is common in most inland waterbodies in non-polar regions) is unexplored. In this paper, we document the possibility to use drone-borne and water-coupled GPR in several cross-sections located in three different waterbodies (1 lake and 2 rivers) in Denmark. These waterbodies had different bed sediment materials and vegetation conditions, an electric conductivity varying from 200 to 340 µS/cm and depths up to 2.5 meters. Drone-borne GPR showed accuracy similar to water-coupled GPR when compared to RTK GNSS ground-truth measurements, with a Mean Absolute Error (MAE) of approx. 8 cm. The only limitations of drone-borne GPR were i) worse minimum depth measurement capability (typically 0.8–1.1 m for drone-borne GPR, while 0.3–0.4 metres for water-coupled GPR) ii) requirement to fly the GPR antenna at altitudes of approx. 0.5 meters above the water surface to avoid high spreading losses and strong surface clutter events hiding the signal. Finally, GPR measurements were benchmarked against traditional sonar measurements, showing that GPR measurements significantly outperform sonar measurements in waterbodies with medium or high density of aquatic vegetation.

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Section: Sonarmentioning

confidence: 99%

Mapping inland water bathymetry with Ground Penetrating Radar (GPR) on board Unmanned Aerial Systems (UASs)

Bandini

Kooij

Mortensen

et al. 2021

Preprint

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“…It also eliminates the need for spatial smoothing and diagonal loading, since the method does not have enough freedom to create signal suppression. This means that it is signi cantly faster and may perform better than MVDR (Blomberg. et al, 2013;.…”

Section: Low Complexity Adaptive Beamformingmentioning

confidence: 99%

“…The related robust and computationally inexpensive "Low Complexity Adaptive" (LCA) beamformer has also been proposed in medical ultrasound and used for sector-scanning and other sonars . It provides a large part of the gain of the Capon beamformer while having a computational load and robustness close to DAS (Blomberg. et al, 2013;.…”

Section: Improving Swath Sonar Water Column 1 Introductionmentioning

confidence: 99%

Improving Swath Sonar Water Column Imagery and Bathymetry With Adaptive Beamforming

Lønmo

Austeng

Hansen

2020

IEEE J. Oceanic Eng.

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Modern sonars provide high-resolution acoustic imaging for a range of applications, including pipeline inspection, harbor surveys and seabed mapping. Central in the signal processing of the sonar data is the beamforming, where conventional (xed) aperture tapering (or weighting) typically is used to set the equipment's resolution and robustness to appropriate values. This means that there are prede ned weights, not necessary optimally chosen, that express a compromise between resolution and sensitivity to noise and interference. In this work, we show how we can increase the resolution of bottom detections, without reducing the robustness, by applying Low Complexity Adaptive (LCA) beamforming. LCA can be viewed as a low computational cost version of the minimum variance distortionless response (MVDR) beamformer. It is easy to implement and improves the imaging process by adaptively choosing an appropriate weight for any point in time and space. We apply LCA beamforming on measured and simulated data. The simulated data are generated by the freely available ultrasound simulator "Field II". The measured data was collected by a commercial echosounder. LCA beamforming gives signi cant reduction of the mainlobe width and sidelobe level over conventional beamforming. LCA also gives clear improvements for amplitude detections, and phase detections on measured data. The phase detections on simulated data are partially worse. We expect to get the same improvement for both datasets after minor modi cations of the LCA algorithm.

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“…However, in most active sonar applications, the echoes from targets are strongly correlated or even coherent, as echoes are usually the scaled and delayed replicas of the transmitted signal. In this case, ABFs would suffer from a dramatic performance degradation as a result of the undesirable rank deficient problem of the signal covariance matrix [5][6][7][8]. One kind of classic preprocessing method applied for adaptive beamforming in coherent scenarios is the spatial smoothing (SS) technique [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multiple‐input multiple‐output sonar adaptive beamforming using transmission diversity smoothing and backward processing

Fan

Liu

Sun

2022

IET Radar Sonar & Navi

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Benefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple-input multiple-output (MIMO) sonar applications. Increasing the number of transmitted signals helps improve the TDS-based decorrelation performance. However, a large number of transmitters is not feasible for those sonar systems mounted on small platforms with limited space. To address the lack of TDS-based degrees of freedom for smoothing, an improved TDS decorrelation method is proposed for MIMO sonar in this study. With the conjugated MIMO data covariance matrix being left-multiplied and right-multiplied, respectively, by the exchange matrix, the authors obtain a backward-smoothed data covariance matrix. Afterwards, the authors average it with the original MIMO receiving data covariance matrix to reconstruct a new data covariance matrix. Compared with TDS, the improved TDS strengthens the decorrelation performance by increasing the number of covariance matrix for smoothing and thus enables the new rank restored signal covariance matrix to have better non-singularity. Without increasing any aperture loss and system complexity, a lower correlation between echoes is obtained, and the performance of the minimum variance distortionless response beamformer is hence improved in the presence of coherent targets. The effectiveness of the proposed decorrelation method is verified by numerical simulations and real data from a water tank experiment. K E Y W O R D S adaptive beamforming, coherent targets, MIMO sonar, transmission diversity and backward smoothingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Improving Sonar Performance in Shallow Water Using Adaptive Beamforming

Cited by 50 publications

References 24 publications

Mapping inland water bathymetry with Ground Penetrating Radar (GPR) on board Unmanned Aerial Systems (UASs)

Mapping inland water bathymetry with Ground Penetrating Radar (GPR) on board Unmanned Aerial Systems (UASs)

Improving Swath Sonar Water Column Imagery and Bathymetry With Adaptive Beamforming

Multiple‐input multiple‐output sonar adaptive beamforming using transmission diversity smoothing and backward processing

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