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In synthetic aperture imaging, the interpulse Doppler shift is commonly used, while the intrapulse Doppler shift is ignored. However, for the linear frequency modulation signal, the intrapulse Doppler shift can cause an additional range cell migration (ARCM) and a phase error term. Additionally, the ARCM may exceed a range resolution cell in certain cases of multireceiver synthetic aperture sonar (SAS), which will result in image defocus. Therefore, the intrapulse Doppler shift in multireceiver SAS should not be ignored in such cases. In this study, the condition of the negligible intrapulse Doppler shift is presented, that is, the moving distance of the sonar platform within a pulse should be much less than an azimuth resolution cell. Based on the multireceiver accurate time-delay model, a multireceiver SAS intrapulse Doppler shift signal model is developed by introducing the intrapulse Doppler frequency into the echo signal. After simplifying the accurate range history under the narrow-beam assumption, the range-Doppler spectrum is derived. Finally, a multireceiver SAS chirp scaling algorithm considering the intrapulse Doppler shift (IDS-CSA) is derived. The proposed algorithm can correct the ARCM and compensate the phase error (although not required), and then obtain a well-focused synthetic aperture image. The effectiveness of the proposed model and algorithm is verified by simulations and field data. The results show that the proposed algorithm provides a better image quality than the conventional multireceiver CSA when the condition of the negligible intrapulse Doppler shift is not met.

Synthetic aperture sonar is one kind of high resolution imaging sonar, which has been widely used in both military and civil applications. However, with the continuous improvement of imaging resolution, the amount of data processed per unit time is increasing, which seriously affects the application of synthetic aperture sonar. After analyzing the characteristics of each processing step of range Doppler imaging algorithm, such as range compression, fixed phase compensation, range cell migration compensation and azimuth compression, an efficient imaging algorithm in heterogeneous environment is proposed in this paper. CPU is mainly responsible for the control of the imaging process, while GPU is responsible for the intensive computation during the imaging processing process, which jointly realize the improvement of imaging efficiency. In order to eliminate the limitation of GPU memory, we proposed a blocking strategy, which can effectively improve the adaptability of the proposed imaging algorithm. We divide the key processing steps into blocks according to the data, upload the local data blocks to GPU memory in turn, and download them to the host memory after been processed, which can effectively avoid the limitation of GPU memory for large data processing. In order to verify the performance of the proposed algorithm, simulation imaging experiment is used to verify the correctness of the parallel imaging algorithm, real synthetic aperture sonar data imaging experiments are used to verify the efficiency of the proposed algorithm, and blocked parallel imaging processing with different number of pulse is simulated to verify the correctness and efficiency. The experimental results show that the real time acceleration rate of the proposed algorithm is above 14, which can meet the signal processing requirements of real time synthetic aperture sonar imaging system.

The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a pretrained model, the experimental results demonstrate that our simple yet effective method helps to achieve reasonably accurate sonar targets segmentation results.

The compensation of slant range motion errors needs to be carried out after range pulse compression in the signal processing of multiple-receiver synthetic aperture sonar (SAS). Whereas range pulse compression cannot be performed as the first step in multiple-receiver SAS chirp scaling (CS) algorithm, as it requires the transmitted signal to be linearly frequency modulated. For this reason, it is difficult to combine CS algorithm with motion compensation. Besides, those imaging algorithms based on interpolation, such as range-Doppler algorithm and wavenumber domain algorithm, have plenty of computation load and large phase errors. Aimed at these problems, a CZT algorithm for multiple-receiver SAS in the non-stop-hop-stop mode is proposed in this paper. The proposed algorithm can correct range cell migration utilizing chirp-z transform, which avoids interpolation. Furthermore, the algorithm is easily combined with motion compensation as it can process pulse-compressed data directly. This paper describes the algorithm in 9 key steps, including the principle of correcting range cell migration utilizing chirp-z transform in discrete domain. In the simulation and sea trial data processing, the proposed algorithm provides image quality equal to the CS algorithm, and the algorithm is slightly more efficient than CS algorithm when combined with motion compensation.

Two-dimensional phase unwrapping is a crucial step in interferometric signal processing. A phase quality map can help the unwrapping algorithm deal with low-quality and fast-changing regions. However, because existing algorithms cannot calculate a quality map representing the gradient quality directly, it is usually necessary to approximate the gradient quality with phase quality to assist the network-based phase unwrapping algorithm. Furthermore, they cannot withstand intense noise in low-quality regions, resulting in many errors in path-based algorithms. To address the aforementioned issues, this paper analyzes the essence of a quality map and proposes a quality map generation method based on a convolutional neural network. The generated quality maps are a pair, each indicating the quality of horizontal and vertical gradients. Experiments show that the quality map generated by this method can help path-based and network-based algorithms perform better.

The existing imaging algorithms for the multiple-hydrophone synthetic aperture sonar (SAS) cannot obtain a good imaging result in the moderate squint case where the squint angle is from 10° to 20°, because they do not take the differential range curvature into account, and the existing methods of equivalent spatial sampling cannot work well. This study proposes an imaging algorithm for the moderate squint multiple-hydrophone SAS. First, the range history model of the moderate squint multiple-hydrophone SAS is established. Second, the signal of each hydrophone is processed separately with bistatic imaging algorithm to correct the differential range curvature. Third, the corrected signal of each hydrophone is shifted to the same range bin to compensate the relative range offset, and is added coherently together to remove the azimuth aliasing and to reconstruct the image without loss of resolution. Fourth, a new method of equivalent azimuth sampling is developed, and a PRF adjusted with squint angle is given. Finally, the validity of the proposed algorithm is tested by the simulation and real data.

Aiming to the problem of losing detailed information of Guass pre-filter in traditional partial differential equation (PDE) method, a speckle reduction method based on coupled PDE for synthetic aperture sonar (SAS) images is proposed in this paper. The proposed method introduce edge strength function in filter processing, which can keep the edge as same as initial image in filter. The evaluation has been carried out using the equivalent number of looks (ENL), edge preserve index(EPI) and image variance as the criteria. Through speckle reduction experiment of real SAS image, comparing the result of proposed method with traditional PDE method and classical Lee filter method, the efficiency of the proposed method is verified.

A hierarchical quality-guided phase unwrapping algorithm in a shared memory environment is proposed. First, the wrapped phase is divided into regular blocks, and local wrap counts of every block are obtained by a quality-guided strategy. Then, the gradient of the block wrap counts of adjacent blocks and the quality of every block are defined by the boundary local wrap counts of every block. Each block's data can be regarded as an abstract phase point, and the quality-guided strategy can be used again to solve the block wrap counts of each block. Finally, the absolute wrap counts of each phase point are obtained by adding local wrap counts and corresponding block wrap counts, and then the final unwrapped phase is obtained. The performance of the proposed algorithm is verified through an unwrapping experiment performed on simulated data and the real interferometric synthetic aperture sonar wrapped phase in a shared memory environment. The results show that the proposed method greatly improves phase unwrapping efficiency while maintaining the correctness of unwrapped results.

For the imaging of multiple-receiver synthetic aperture sonar (SAS) under the nonstop-hop-stop case, an exact analytical solution for the two-dimensional (2-D) frequency spectrum is usually difficult to obtain because of the existence of the double-square-root (DSR) term in the range history equation. Several approximate solutions for the 2-D spectrum have been derived to focus the multiple-receiver SAS data. In this paper, the range history geometry for multiple-receiver SAS is newly constructed to meet the demand of the derivation of an analytical 2-D spectrum. According to the geometry-based bistatic formula (GBF) method, a quasi-analytical 2-D spectrum with an unknown variable named half quasi-bistatic angle (HQBA) is reviewed. Based on the method of series reversion (MSR), the fourth order equation with respect to the HQBA is solved and an analytical HQBA is obtained. After substituting the analytical HQBA into the quasi-analytical 2-D spectrum, a completely analytical 2-D spectrum is obtained and the problem of the 2-D spectrum derivation caused by the DSR term is solved successfully. In this paper, an extended range Doppler (RD) algorithm based on the derived 2-D spectrum is proposed for focusing the multiple-receiver SAS data under the non stop-hop-stop case. The results of simulation and real experiment data imaging have validated the effectiveness and accuracy of the proposed algorithm.

The low efficiency of complex image registration has become a key problem for the signal processing of interferometric synthetic aperture sonar (InSAS). In this paper, a collaborative complex image registration method based on CPU/GPU is proposed, which provides a new method of realtime complex image registration for InSAS. The processing steps of coarse registration, salve image interpolation and wrapped phase extraction are mapped to graphics processing unit (GPU) and executed respectively. The refine registration and polynomial coefficient fitting are still executed on CPU. The complex image registration test performed on real InSAS data confirms the validity and efficiency of the proposed method.

The effect that motion errors have on synthetic aperture sonar (SAS) imagery can be severe, and motion compensation (MOCO) is one of the difficult problems in SAS imaging. This paper studies the compensation of trajectory deviations and imaging algorithms in SAS. The range history model and motion error expression of multiple-receiver SAS deviating from the nominal trajectory are derived in the nonstop-and-hop mode and the condition of neglecting the aperture dependence of motion errors is given. With the analysis of the range space variance of motion errors, it is found that motion errors, under the condition of typical SAS parameters, are far greater than one range resolution cell because of the wide relative swath, and the range space variance of range position errors cannot be neglected. Most of the existing MOCO methods that account for known deviations of the platform trajectory in SAS are combined with the time-domain beamforming algorithm, which is computationally expensive. And a few are combined with block reconstruction algorithms, but draw on the two-step MOCO in SAR and are not suitable for SAS. This paper proposes a MOCO method that the range position errors are compensated by interpolation after range compression and the corresponding phase errors are compensated by phase multiplication. Then the wide-swath MOCO and imaging are realized by combining the proposed MOCO method with CZT algorithm. Simulated data sets validate the effectiveness of the proposal.

Squint multi-receiver synthetic aperture sonar (SAS) data is more challenging to process than its common side-looking counterpart because its geometry is more complicated. While traditional multi-receiver algorithms handle general side-looking cases, they do not work well for squint cases. No multi-receiver algorithm for handling squint multi-aperture SAS data has been reported. This paper focuses on the imaging algorithm of the squint multi-receiver SAS. Firstly, the “non-stop-go-stop” signal model is established for the squint multi-receiver, and the fourth-order Taylor expansion is used to approximate the accurate instantaneous slant range. Secondly, the system transfer function in the 2-D frequency domain is given. Thirdly, based on the 2-D frequency domain, a new range Doppler algorithm is applied to the squint multi-receiver SAS. Finally, simulations show that the algorithm can handle high squint multi-receiver data.

Imaging efficiency has become a key problem for the application of synthetic aperture sonar (SAS) as the volume of raw data to be processed increases. In order to solve the imaging problem of low efficiency, a fast parallel range Doppler imaging algorithm for synthetic aperture sonar based on multicore platform with shared memory is presented. The time-consuming processing steps such as range compression, fixed phase compensation, range cell migration compensation and azimuth compression are parallelized by OpenMP instructions in the shared memory environment, which greatly speeds up the imaging process. The imaging tests performed on simulated and real SAS data confirm the validity and efficiency of the proposed method.

Phase unwrapping is one of the key problems in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) or interferometric synthetic aperture sonar (InSAS) data. In this letter, we propose a quality-guided and local minimum discontinuity based phase unwrapping algorithm, which enhances the precision of the unwrapped result by local minimum discontinuity optimization in low quality areas, and still keeps a high efficiency. The new algorithm can be divided into two steps. Firstly, the quality-guided phase unwrapping algorithm is performed on the original wrapped phase image, which helps to remove all the discontinuities caused by the wrapping operation quickly, but tends to spread the unwrapped errors in low quality areas. Secondly, the initial unwrapped result is divided into high and low quality areas according to its quality map, and the minimum discontinuity optimization process is performed in the low quality areas of the initial unwrapped result, which helps to remove the remaining improving loops defined as a loop with more positive jumps than negative ones. This prevents the unwrapped errors spreading from low quality areas to high quality areas and accelerates the optimization process by restricting the optimization place in low quality areas. Tests performed on InSAR and real InSAS data confirm the accuracy and efficiency of the proposed algorithm.

Solid-state fermentation is a dynamic process involved with complex microbiome. Microbial structure and succession significantly affect the yield and quality of fermentation productions. Although the importance of microbial structure was extensively studied, the significance of microbial succession rate remains unclear in solid-state fermentation. To address this gap, we designed an in situ experiment in a typical distillery to characterize the effects of microbial succession rate. In this study, we found the process of strong-aroma Baijiu making could be divided into two stages according to fermentation parameters (starch, moisture, acidity, reducing sugar, alcohol, temperature). The early stage showed significantly (p 0.05) higher microbial diversity than that of the later stage according to Shannon index. Compared with single cereal fermentation, mixed cereals fermentation showed slower microbial succession rate of stage shift. We found that Lactobacillus could reflect microbial succession rate of stage shift in strong-aroma Baijiu fermentation. Meanwhile, we found fermentation parameters could affect microbial succession rate of stage shift. Microbial diversity was significantly (p 0.05) correlated with fermentation parameters. Moreover, molecular ecological network analysis (MENA) showed that succession rate of microbial community could affect microbial interactions. In addition, fermentation of mix cereals (sorghum, wheat, corn, rice and glutinous rice) increased the enrichment of Clostridiales from pit mud according to results of source tracking. Collectively, succession rate of microbial community could be an important trait to explain differences of microbial diversity and flavor profile from the perspective of microbial decline and enrichment. Our study highlighted the importance of microbial succession rate during strong-aroma Baijiu making process and provided a dynamic perspective to observe solid-state fermentation.

Phase unwrapping is a very important process in the digital elevation model(DEM) rebuilding of the imaging area from its interferometric phase data. This paper presents a fast phase unwrapping algorithm based on minimum discontinuity optimization, which greatly enhances the efficiency compared with the original algorithm proposed by Flynn. To accelerate the optimization process, a preprocessing process is used to get an initial wrap count by the least-squares method. For that the unweighted least-squares problem can be solved by FFT-based method, the initial wrapped count can be obtained quickly. Then the middle unwrapped result can be obtained by the initial wrap count, and the circle canceling method is used to remove the remaining discontinuities in the middle unwrapped result. Tests with simulated and real data verify the accuracy and efficiency of the proposed algorithm.