High‐resolution imaging for the multireceiver SAS

Zhang, Xuebo; Ying, Wenwei; Dai, Xuntao

doi:10.1049/joe.2019.0431

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…By introducing a virtual transducer in the middle position of each bistatic SAS system [26,27,54], the equivalent slant range…”

Section: A Pca Methodsmentioning

confidence: 99%

A Comparison of PCA and LBF Based Multireceiver SAS Imaging Algorithms

Wang,

Huang

2023

IEEE Access

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Multireceiver synthetic aperture sonar (SAS) is a useful tool to provide high-resolution images. The most important step lies in developments of multireceiver synthetic aperture image formation processors, which uses the sonar echoed signal as the input. The system transfer function is crucial in the development of signal processors. In order to use traditional imaging algorithms, the monostatic conversion transferring multireceiver SAS signal to monostatic SAS data is often conducted. A review and evaluation of imaging methods based on monostatic conversion is conducted in this article. The relationship of Phase center approximation (PCA) method and Loffeld's bistatic formula (LBF) is firstly reformulated in theory. Considering facts that PCA and LBF methods are the approximation of exact system transfer function, the approximation error of both methods are firstly compared and evaluated by using the numerical system transfer function, which is considered to be the precise system transfer function. Since more approximations of PCA and LBF should be carried out to develop fast imaging algorithms, the actual phase error of both methods is compared and evaluated based on numerical system transfer function. Then, the imaging performance of both methods based on range migration algorithm (RMA) is further evaluated. At last, the relationships of both patent methods are comprehensively described.

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“…By introducing a virtual transducer in the middle position of each bistatic SAS system [26,27,54], the equivalent slant range…”

Section: A Pca Methodsmentioning

confidence: 99%

A Comparison of PCA and LBF Based Multireceiver SAS Imaging Algorithms

Wang,

Huang

2023

IEEE Access

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“…Considering that the expression of R * i (t; r) is very complex, it is necessary to simplify it. According to the description of the displaced phase center antenna (DPCA) technology [16], the multi-hydrophone SAS can be viewed as a monostatic hydrophone transmit signal and receive echo at the phase center of the bistatic transponder/hydrophone pair. Thus, the range history R * i (t; r) can be considered as the sum of a single root term and a rangedependent term and is rewritten as follows:…”

Section: Sub-beam Range Historymentioning

confidence: 99%

Two-Dimensional Space-Variant Motion Compensation Algorithm for Multi-Hydrophone Synthetic Aperture Sonar Based on Sub-Beam Compensation

Wu,

Zhou,

Xie

et al. 2024

Remote Sensing

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For a multi-hydrophone synthetic aperture sonar (SAS), the instability of the platform and underwater turbulence easily lead to two-dimensional (2-D) space-variant (SV) motion errors. Such errors can cause serious imaging problems and are very difficult to compensate for. In this study, we propose a 2-D SV motion compensation algorithm for a multi-hydrophone SAS based on sub-beam compensation. The proposed algorithm is implemented using the following four-step process: (1) The motion error of each sub-beam is obtained by substituting the sonar’s motion parameters measured in the exact motion error model established in this study. (2) The sub-beam’s targets of all targets are compensated for motion error by implementing two-phase multiplications on the raw data of the multiple-hydrophone SAS in the order of hydrophone by hydrophone. (3) The data of the sub-beam’s target compensated motion error are extracted from the raw data by utilizing the mapping relationship between the azimuth angle and the Doppler frequency. (4) The imaging result of each sub-beam is obtained by performing a monostatic imaging algorithm on each sub-beam’s data and coherently added to obtain high-resolution imaging results. Finally, the validity of the proposed algorithm was tested using simulation and real data.

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“…When using SSS or other sonar imaging systems for underwater exploration, the receiving waveform and transmitting template will greatly differ due to the uneven frequency response of the transducer and insufficient power when transmitting long pulses. To obtain a higher range resolution, a common signal processing method is to match the data collected by SSS, namely pulse compression technology [22][23][24]. However, conventional pulse compression techniques have limited range resolution and an increased sidelobe; in the case of strong interference, the influence of the sidelobe is more obvious and even affects the accuracy of autonomous detection.…”

Section: Introductionmentioning

confidence: 99%

An Image Quality Improvement Method in Side-Scan Sonar Based on Deconvolution

Liu,

Pang,

Yan

et al. 2023

Remote Sensing

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Side-scan sonar (SSS) is an important underwater imaging method that has high resolutions and is convenient to use. However, due to the restriction of conventional pulse compression technology, the side-scan sonar beam sidelobe in the range direction is relatively high, which affects the definition and contrast of images. When working in a shallow-water environment, image quality is especially influenced by strong bottom reverberation or other targets on the seabed. To solve this problem, a method for image-quality improvement based on deconvolution is proposed herein. In this method, to increase the range resolution and lower the sidelobe, a deconvolution algorithm is employed to improve the conventional pulse compression. In our simulation, the tolerance of the algorithm to different signal-to-noise ratios (SNRs) and the resolution ability of multi-target conditions were analyzed. Furthermore, the proposed method was applied to actual underwater data. The experimental results showed that the quality of underwater acoustic imaging could be effectively improved. The ratios of improvement for the SNR and contrast ratio (CR) were 32 and 12.5%, respectively. The target segmentation results based on this method are also shown. The accuracy of segmentation was effectively improved.

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High‐resolution imaging for the multireceiver SAS

Cited by 8 publications

References 16 publications

A Comparison of PCA and LBF Based Multireceiver SAS Imaging Algorithms

A Comparison of PCA and LBF Based Multireceiver SAS Imaging Algorithms

Two-Dimensional Space-Variant Motion Compensation Algorithm for Multi-Hydrophone Synthetic Aperture Sonar Based on Sub-Beam Compensation

An Image Quality Improvement Method in Side-Scan Sonar Based on Deconvolution

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