Correction Method for Magnitude and Phase Variations in Acoustic Arrays Based on Focused Beamforming

Yuan, Weijia; Zhou, Tian; Shen, Jiajun; Du, Weidong; Wei, Bo; Wang, Tianhao

doi:10.1109/tim.2020.2972657

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…In particular, the specific derivation of the estimation of the receiver covariance matrix is not described here, and it can be viewed in previous studies [20]. It can be denoted as:…”

Section: Mathematic Modelmentioning

confidence: 99%

“…It is worth noting that mutual coupling can also be transformed into amplitude and phase errors [19]. Gain errors are estimated using the main diagonal of the actual covariance matrix of the virtual uniform linear array (ULA), while phase errors can be determined through the Toeplitz structure of the ideal covariance matrix [20]. The symmetric Toeplitz structure of the MCM plays a crucial role in solving these challenges [21].…”

Section: Introductionmentioning

confidence: 99%

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The Mitigation of Mutual Coupling Effects in Multi-Beam Echosounder Calibration under Near-Field Conditions

Zhang,

Yuan,

Sun

et al. 2024

JMSE

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The advancement of unmanned platforms is driving the miniaturization and cost reduction of the multi-beam echosounder (MBES). In the process of MBES array calibration, the mutual coupling significantly impacts the performance of parameter estimation. We propose a correction method to mitigate the mutual coupling effects in the calibration of MBES acoustic array. Initially, a near-field focused beamforming model is established to assess the influence of mutual coupling. Subsequently, the covariance matrix in the frequency domain is constructed to enhance algorithm efficiency and simplify solution procedures. This construction eliminates the need for a low-pass filtering step after heterodyning through extracting peak values near zero frequency in the signal frequency domain. Meanwhile, the Toeplitz property is leveraged to render the estimation results independent of the mutual coupling matrix. Finally, the mutual coupling coefficients and the direction of arrival (DOA) are joint-estimated and the Cramér–Rao bound is derived. The presented method effectively addresses the engineering challenge of MBES mutual coupling calibration. Additionally, the performance of the proposed method is verified through the measured data in simulation and tank experiments.

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“…In particular, the specific derivation of the estimation of the receiver covariance matrix is not described here, and it can be viewed in previous studies [20]. It can be denoted as:…”

Section: Mathematic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Mitigation of Mutual Coupling Effects in Multi-Beam Echosounder Calibration under Near-Field Conditions

Zhang,

Yuan,

Sun

et al. 2024

JMSE

Self Cite

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“…An array of ultrasonic sensors can achieve much higher resolution than can be achieved from a single ultrasonic sensor [ 26 , 27 ]. For reception (RX), this was achieved by combining the 12 microphone receiver channel signals into a single channel of data using beamforming.…”

Section: Processing Array Datamentioning

confidence: 99%

Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array

Parr

Legg

Bradley³

et al. 2021

Sensors

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Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.

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“…The estimation of errors in the collected data enables us to mitigate the adverse effects of noise on the data by using the least-squares method. Although the performance of the multi-beam system is constantly being improved, such as the newest multi-beam sonar, which automatically compensates for the errors to maintain the instrument resolution [6,7], the depth error constantly exists, which is mainly caused by the abnormal seabed detection algorithm. Some algorithms that try to eliminate outliers in real-time processing.…”

Section: Introductionmentioning

confidence: 99%

A quality factor of forecasting error for sounding data in MBES

Yuan

Zhou

et al. 2022

Meas. Sci. Technol.

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The multi-beam sounding system achieves ultra-wide coverage and high-resolution measurement. Its significantly increased data density has great advantages in accurately depicting the topography of the seabed. However, it requires processing large amount of data. A preprocessing method that performs in real time, automatically identifies the outliers in multi-beam bathymetry data, and provides corresponding bathymetry estimates, is able to provide a lot of effective information for the post-processing for improving the data processing efficiency and ensuring data quality. In this work, we propose quality factor of forecasting error (QE) for detecting the outliers and forecasting the depth in multi-beam bathymetry data. On the basis of the existing quality factor model for seabed detection method, and under the assumption of smooth seafloor terrain, we use the quality factor (QF) to select a suitable seabed detection method and eliminate the sounding points which correspond to poor echo characteristics. The uncertainty inferred by the quality factor is used as the initial parameter of Kalman filter estimation and the depth value prediction model is formulated. The sounding sequence is sorted by the median value by using the sliding window method. After second fitting and Kalman filtering, the depth of each point is predicted. The quality factor model based on forecasting errors is adopted to simplify and unify the outlier detection standards. The selection rules of window length and detection threshold are deeply studied on the basis of simulations performed in this work. For appropriate parameters, the proposed algorithm shows a good detection capability for impulse anomalies, cluster anomalies, and seabed topography undulations. In addition, the proposed method gives smooth depth prediction values. The simulation results and analysis show that the proposed algorithm further detects the outliers in depth sequence on the basis of QF and forecasts the sounding point in real time. The QE threshold based on the relative depth is easy to select and is suitable for different sounding system. It provides effective outlier detection information for post-processing.

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Correction Method for Magnitude and Phase Variations in Acoustic Arrays Based on Focused Beamforming

Cited by 9 publications

References 22 publications

The Mitigation of Mutual Coupling Effects in Multi-Beam Echosounder Calibration under Near-Field Conditions

The Mitigation of Mutual Coupling Effects in Multi-Beam Echosounder Calibration under Near-Field Conditions

Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array

A quality factor of forecasting error for sounding data in MBES

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