Deconvolution super beam forming method and system for multi-beam sonar image

Abstract

The invention discloses a deconvolution super beam forming method and system for a multi-beam sonar image, and belongs to the field of sonar image processing. Collecting multi-beam sonar data, and creating a target characteristic database; generating a point spread function for deconvolution by using a super beam forming method; the azimuth spectrum is optimized through a Richardson-Lucy iterative algorithm; adjusting algorithm parameters, and verifying the performance of the optimized Dcv-HBF algorithm through simulation and experiments; and outputting a clear target sonar image for target detection and background noise suppression analysis. According to the method, the principle of hyper-beam forming HBF and the principle of deconvolution conventional beam forming Dcv-CBF are combined, an HBF beam pattern serves as a point spread function PSF, deconvolution is conducted on the orientation spectrum of the HBF through the R-L iterative algorithm, the spatial spectrum of Dcv-HBF is generated, and the spatial resolution and background noise suppression capacity of sonar imaging are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sonar signal processing and high-resolution imaging, and in particular to a deconvolution hyper beamforming method (Dcv-HBF) and system for multi-beam sonar images, which are intended to improve spatial resolution, enhance target characteristic expression and background noise suppression capabilities. Background Art

[0002] Imaging technology based on multi-beam sonar is to obtain high-resolution images of underwater environments by receiving and processing the sound wave signals reflected by the target. This technology is widely used in the fields of seabed terrain detection, underwater target identification, marine ranch monitoring and underwater search and rescue, and is an important tool for current underwater survey and research. Multi-beam imaging sonar can provide spatial distribution and characteristic information of targets with high efficiency, but its beamforming method directly affects the imaging resolution and target identification accuracy.

[0003] The traditional multi-beam imaging sonar beamforming algorithm is based on conventional beamforming (CBF), which performs phase compensation and weighted superposition of the acoustic signals received by the array to maximize the response in a specific direction. However, the CBF algorithm has the problems of large main lobe width and high side lobe level, which leads to limited spatial resolution of the target and difficulty in separating weak target signals from background noise. These problems are particularly obvious in complex environments, especially when the signal-to-noise ratio is low, the performance of CBF is significantly reduced, affecting the accurate positioning and identification of the target.

[0004] In recent years, Hyper Beamforming (HBF), as an improved method based on "sum beam" and "difference beam" technology, has shown good resolution and sidelobe suppression effects. By splitting the array and constructing a higher-order beam pattern, HBF achieves sharpening of the main lobe and reduction of the side lobe. However, the sidelobe level of HBF in a low signal-to-noise ratio environment is still high, and the background noise suppression ability is limited. In addition, due to the characteristics of the beam pattern, its spatial spectrum does not have translation invariance, which affects the deconvolution processing effect in practical applications.

[0005] To further improve the resolution, the deconvolution conventional beamforming (Dcv-CBF) method was introduced into multi-beam imaging sonar. This method restores the real spatial distribution of the target by treating the CBF beam pattern as a point spread function (PSF) and processing the azimuth spectrum in combination with a deconvolution algorithm (such as the Richardson-Lucy algorithm). Dcv-CBF significantly improves the resolution and reduces the background noise level, but it has the defect of being sensitive to noise when processing low signal-to-noise ratio data. In addition, Dcv-CBF is prone to blurring and distortion in the edge area of ​​the target, affecting the overall imaging quality.

[0006] In summary, the main challenges faced by multi-beam imaging sonar beamforming algorithms include: how to effectively suppress side lobes and background noise in low signal-to-noise ratio environments, how to further improve the resolution of the main lobe, and how to avoid target edge blur and information loss. Therefore, a new beamforming method that combines hyperbeamforming and deconvolution technology is needed to achieve higher quality sonar imaging and meet application requirements in complex environments. Summary of the invention

[0007] In order to solve the technical problems existing in the background technology, the present invention provides a deconvolution super beamforming method and system for multi-beam sonar images to improve imaging resolution, enhance target recognition capability and reduce background noise.

[0008] In order to solve the technical problem, the technical solution of the present invention is:

[0009] A deconvolution super beam forming method for multi-beam sonar images, comprising:

[0010] S1: Collect multibeam sonar data to create a high-quality target characteristics database for algorithm development;

[0011] Sonar data of underwater targets are collected from multi-beam sonar equipment, including different types of underwater targets, such as structures, submersibles, spherical targets, etc. The collected data is pre-processed to remove noise, calibrate sensor errors and calibrate to form a high-quality target characteristic database. This database is used for subsequent algorithm development and performance verification.

[0012] S2: Generate point spread function for deconvolution using hyperbeamforming method;

[0013] Based on the hyperbeamforming (HBF) method, the point spread function (PSF) for deconvolution is generated by optimizing the preliminary spatial spectrum obtained by the conventional beamforming (CBF) algorithm. Hyperbeamforming divides the receiving array into multiple subarrays to improve the beam directivity and target resolution, and further enhance the target characteristics in the spatial spectrum. This step can effectively improve the detail performance of the target and provide an accurate PSF for the deconvolution process.

[0014] S3: Optimize the azimuth spectrum and improve the resolution through the Richardson-Lucy iterative algorithm;

[0015] The azimuth spectrum obtained by super beamforming is deconvolved using the Richardson-Lucy (RL) iterative algorithm. The algorithm iteratively corrects the blurred parts in the image, gradually restores the details of the image and improves the spatial resolution of the image. In each iteration, the algorithm uses the PSF generated by deconvolution to repair the blurred areas in the spatial spectrum, optimize the edge sharpness of the target, and improve the accuracy of target recognition.

[0016] S4: Adjust algorithm parameters and verify the performance of the optimized Dcv-HBF algorithm through simulation and experiments;

[0017] The optimized Dcv-HBF algorithm is parameterized and its performance verified through simulation and experimental data. Under different environmental conditions, by comparing simulation data and experimental results, key parameters in the algorithm, such as the number of iterations, regularization factor, and beamforming parameters, are optimized to ensure the robustness and efficiency of the algorithm in different noise environments. The purpose of this step is to further improve the performance of the algorithm in practical applications and ensure its effectiveness in underwater target detection.

[0018] S5: Output clear target sonar images for target detection and background noise suppression analysis;

[0019] The optimized Dcv-HBF algorithm generates clear sonar images of underwater targets, with sharp target edges, clear details, and effectively suppressed background noise. The generated images are used for subsequent target detection, recognition, and background noise suppression analysis. The images can be used in environmental monitoring, seabed exploration, and other fields to ensure accurate recognition and analysis of underwater targets.

[0020] Data collection uses multi-beam forward-looking sonar as the acoustic image acquisition device. The collected sonar data includes underwater structures, submersibles, fish schools and other targets. The data collection process undergoes noise suppression and sensor calibration to ensure data quality.

[0021] In step S1, the collected multi-beam sonar data is subjected to noise removal, calibration and calibration to generate a target characteristic database, which covers a variety of target image information under different water depths and environmental conditions, supporting subsequent algorithm development and performance evaluation.

[0022] In step S2, the super beamforming algorithm optimizes the conventional beamforming (CBF) algorithm and adopts a sub-array method to perform multi-beam combination on the received signal, thereby generating an accurate point spread function (PSF) and providing high-quality input data for the deconvolution process.

[0023] In step S3, the Richardson-Lucy (RL) iterative algorithm is used to optimize the spatial spectrum through deconvolution operations, gradually recovering image details, especially the edge information of the target. The algorithm modifies each pixel value through multiple iterations, significantly improving the spatial resolution of the image.

[0024] In step S4, by comparing simulation and experimental data, key parameters in the Dcv-HBF algorithm, including regularization parameters, number of iterations, and beamforming weights, are optimized to improve the robustness of the algorithm in complex noise environments and ensure accurate detection of targets.

[0025] In step S5, the generated image can be effectively used for underwater target detection and background noise suppression by eliminating noise and improving target detail performance, thereby improving the accuracy and reliability of target recognition and being suitable for various application scenarios such as underwater detection and environmental monitoring.

[0026] The present invention is applicable to different underwater environments, and can maintain good target recognition capability under conditions of low signal-to-noise ratio and strong noise interference, thereby providing high-quality underwater images.

[0027] The parameter optimization of the deconvolution algorithm of the present invention is ensured to balance computational efficiency and image quality in different noise environments by comparing with actual experimental data.

[0028] The present invention also provides a deconvolution super beamforming system for multi-beam sonar images, the system comprising:

[0029] Data acquisition module: used to extract sonar image data of underwater targets from multi-beam sonar equipment;

[0030] Beamforming module: Uses the conventional beamforming (CBF) algorithm to perform preliminary beamforming and generate a spatial spectrum;

[0031] Hyper Beamforming Module: Hyper Beamforming (HBF) algorithm is used to optimize the spatial spectrum and improve the target resolution;

[0032] Deconvolution processing module: uses Richardson-Lucy (RL) iterative algorithm to deconvolve and optimize the spatial spectrum to improve image clarity;

[0033] Imaging generation module: Generates high-resolution underwater target images based on the optimized spatial spectrum for subsequent target detection and analysis.

[0034] The system also includes a control and coordination module: used for coordinating the execution order of each module to ensure the smooth operation of the deconvolution super beamforming algorithm.

[0035] The system also includes a data storage and management module: used to store original image data and processed image data, provide image data management and retrieval functions, and ensure the effective use of data in subsequent analysis.

[0036] The system also includes a user interface module: used to provide a graphical user interface, allowing the user to input algorithm parameters, view real-time processing results and output the final underwater target image.

[0037] The present invention also provides a computer-readable storage medium on which a computer program is stored. The program can be run on a computing device to implement the deconvolution super beamforming method for multi-beam sonar images.

[0038] The key technical points of the present invention are:

[0039] (1) Combining HBF and deconvolution technology: The HBF beam pattern is used as the point spread function (PSF), and the Richardson-Lucy (RL) deconvolution algorithm is used to iteratively deconvolve the azimuth spectrum to generate a high-resolution target distribution spatial spectrum.

[0040] (2) Optimizing beam pattern characteristics: By splitting the array to generate a “sum beam” and a “difference beam”, the HBF order is adjusted to optimize the main lobe width and side lobe level, thereby enhancing the target feature expression capability.

[0041] (3) Balancing the number of deconvolution iterations: Dynamically adjust the number of iterations to balance the needs of resolution improvement and noise suppression, and avoid the noise enhancement problem introduced by excessive iterations.

[0042] Compared with the prior art, the advantages of the present invention are:

[0043] (1) Improve imaging resolution;

[0044] Deconvolution technology is used to greatly narrow the main lobe width, significantly improving target positioning and resolution capabilities. In simulations and experiments, the main lobe width of the Dcv-HBF algorithm is significantly smaller than that of CBF and Dcv-CBF, and can accurately distinguish adjacent targets with an azimuth difference of only 0.4°.

[0045] (2) Enhance the ability to suppress background noise;

[0046] By optimizing the sidelobe characteristics of the HBF beam pattern and introducing the RL algorithm, Dcv-HBF achieves the best background noise suppression effect under low signal-to-noise ratio conditions. Compared with HBF, the sidelobe level of the present invention is lower and can still provide high-quality spatial spectrum output in extremely low signal-to-noise ratio environments.

[0047] (3) Excellent performance in edge target processing;

[0048] The problem of blurring and distortion in the target edge area of ​​Dcv-CBF is solved. By combining the characteristics of HBF beam pattern, Dcv-HBF avoids the spatial spectrum edge distortion phenomenon without the need for additional spatial expansion operations.

[0049] (4) High flexibility to adapt to complex environments;

[0050] The present invention can dynamically adjust the HBF order and the number of deconvolution iterations to adapt to different array sizes and signal-to-noise ratio environments. In experiments, the method shows good adaptability to environments with different numbers of receiving array elements and signal-to-noise ratios, especially under low signal-to-noise ratio conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is an overall flow chart of the deconvolution hyperbeamforming (Dcv-HBF) algorithm of the present invention;

[0052] Figure 2 A schematic diagram of an array structure based on hyper beam forming (HBF) proposed in the present invention;

[0053] Figure 3 This is a performance comparison diagram of the main lobe and side lobe characteristics of the HBF and CBF proposed in the present invention;

[0054] Figure 4 This is a spatial spectrum output effect diagram generated by simulation experiments in the embodiment under the condition of SNR=30dB signal-to-noise ratio;

[0055] Figure 5 This is a partially enlarged view of the spatial spectrum output effect under the condition of SNR=30dB signal-to-noise ratio generated by simulation experiments in the embodiment;

[0056] Figure 6 This is a spatial spectrum output effect diagram generated by simulation experiments in the embodiment under the condition of SNR=0dB signal-to-noise ratio;

[0057] Figure 7 This is a partially enlarged view of the spatial spectrum output effect under the condition of SNR=0dB signal-to-noise ratio generated by the simulation experiment of the embodiment;

[0058] Figure 8 This is a spatial spectrum output effect diagram generated by simulation experiments in the embodiment under the condition of SNR=-50dB signal-to-noise ratio;

[0059] Fig. 9 This is a partially enlarged view of the spatial spectrum output effect under the condition of SNR=-50dB signal-to-noise ratio generated by the simulation experiment in the embodiment;

[0060] Fig.10 The sonar image obtained by the embodiment based on CBF processing;

[0061] Fig.11 The sonar image obtained by the embodiment based on Dcv-CBF processing;

[0062] Fig.12 This is a sonar image obtained by Dcv-HBF processing in the embodiment;

[0063] Fig.13 This is a noise power comparison diagram under different input signal-to-noise ratios generated by the present invention through simulation experiments;

[0064] Fig.14 This is a comparison diagram of main lobe widths under different numbers of receiving array elements generated by the present invention through simulation experiments;

[0065] Fig.15 The spatial spectrograms of the four algorithms when the target orientation is 70° are generated by the simulation experiment of the present invention;

[0066] Fig.16 The present invention generates a comparison diagram of the spatial part of the four algorithms when the target orientation is 70° through simulation experiments;

[0067] Fig.17 The spatial spectrum output diagrams when the target is at 0° and 0.7° respectively are generated by simulation experiments in the embodiment;

[0068] Fig.18 This is an enlarged view of the spatial spectrum output portion when the target is at 0° and 0.7° respectively, generated by the simulation experiment of the embodiment;

[0069] Fig.19 The spatial spectrum output diagrams when the target is at 0° and 0.4° respectively are generated by simulation experiments in the embodiment;

[0070] Fig. 20 This is an enlarged view of the spatial spectrum output portion when the target is at 0° and 0.4° respectively, generated by the simulation experiment of the embodiment;

[0071] Fig.21 The following is a basic flow chart of beamforming based on FPGA in an embodiment. DETAILED DESCRIPTION

[0072] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0073] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0074] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0075] Embodiment 1:

[0076] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0077] like Figure 1 As shown, a deconvolution super beam forming method for multi-beam sonar images comprises the following steps:

[0078] S1: Collect multibeam sonar data to create a high-quality target characteristics database for algorithm development

[0079] Sonar data of underwater targets are collected from multi-beam sonar equipment, including different types of underwater targets, such as structures, submersibles, spherical targets, etc. The collected data is pre-processed to remove noise, calibrate sensor errors and calibrate to form a high-quality target characteristic database. This database is used for subsequent algorithm development and performance verification.

[0080] S2: Generate point spread function (PSF) for deconvolution using hyperbeamforming method

[0081] Based on the Hyper Beam Forming (HBF) method and the formula PSF(θ) = B HBF (θ), the preliminary spatial spectrum obtained by optimizing the conventional beamforming (CBF) algorithm is used to generate the point spread function (PSF) for deconvolution. Figure 2, super beamforming improves beam directivity and target resolution by dividing the receiving array into multiple sub-arrays, further enhancing the target characteristics in the spatial spectrum. This step can effectively improve the detail performance of the target and provide an accurate PSF for the deconvolution process. The beam pattern and spatial spectrum output of HBF can be obtained by the following formula:

[0082]

[0083] Wherein, n represents the order of HBF, and the main lobe of the HBF spatial spectrum output can be narrowed by reducing n.

[0084] S3: Optimize the azimuth spectrum and improve the resolution by using the Richardson-Lucy iterative algorithm

[0085] The azimuth spectrum obtained by super beamforming is deconvolved using the Richardson-Lucy (RL) iterative algorithm. In the context of Dcv-HBF, the iterative formula of the RL algorithm is expressed as:

[0086]

[0087] Where i represents the iteration round. As the number of iterations increases, the output S of the deconvolution beamforming H (sinθ) gradually converges to the desired spatial distribution. Figure 3 As shown in Figure 1, the algorithm iteratively corrects the blurred parts of the image, gradually recovers the details of the image and improves the spatial resolution of the image. In each iteration, the algorithm uses the PSF generated by deconvolution to repair the blurred areas in the spatial spectrum, optimize the edge sharpness of the target, and improve the accuracy of target recognition.

[0088] S4: Adjust algorithm parameters and verify the performance of the optimized Dcv-HBF algorithm through simulation and experiments

[0089] The optimized Dcv-HBF algorithm is parameter adjusted and its performance verified through simulation and experimental data. Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 As shown in the figure, under different environmental conditions, by comparing simulation data and experimental results, key parameters in the algorithm, such as the number of iterations, regularization factor and beamforming parameters, are optimized to ensure the robustness and efficiency of the algorithm in different noise environments. The purpose of this step is to further improve the performance of the algorithm in practical applications and ensure its effectiveness in underwater target detection.

[0090] S5: Output clear target sonar images for target detection and background noise suppression analysis

[0091] The optimized Dcv-HBF algorithm generates clear sonar images of underwater targets, such as Fig.10 , Fig.11 , Fig.12 As shown in the figure, the target edge in the image is sharp, the details are clear, and the background noise is effectively suppressed. The generated image is used for subsequent target detection, recognition, and background noise suppression analysis. The image can be used in environmental monitoring, seabed exploration and other fields to ensure accurate recognition and analysis of underwater targets.

[0092] Improved technical details:

[0093] 1. Optimized design of super beamforming;

[0094] The further improved HBF algorithm achieves a narrower main lobe width and lower side lobe level by optimizing the linear weights in the “sum beam” and “difference beam” calculations. Fig.13 and Fig.14 As shown, when the target azimuth is 70°, the improved beam characteristics perform well.

[0095] 2. Improvement of deconvolution algorithm;

[0096] RL algorithm combined with noise robustness optimization:

[0097] Dynamic regularization parameter adjustment: According to the local statistical characteristics of the spatial spectrum, the regularization parameters are adjusted in real time to prevent noise amplification during the iteration process.

[0098] Edge feature preservation: By enhancing the edge weight of the PSF, the blurring of the target edge after deconvolution is avoided. Fig.15 , Fig.16 , Fig.17 , Fig.18 , Fig.19 , Fig. 20 As shown, the problem of insufficient target resolution and blurred edges is solved.

[0099] 3. Modular system implementation;

[0100] Construct a deconvolution hyperbeamforming system for multi-beam sonar images, including the following modules:

[0101] Data acquisition module: collects raw data from multi-beam sonar equipment.

[0102] Preliminary beamforming module: Executes the CBF algorithm and performs basic processing on the input data.

[0103] Hyper Beamforming Module: Uses HBF algorithm to generate high-resolution beam patterns.

[0104] Deconvolution optimization module: Use the improved RL algorithm to perform deconvolution processing and output the optimized spatial spectrum.

[0105] Imaging generation module: Generates clear underwater imaging results based on the final spatial spectrum. Fig.21 As shown, the modularization process based on FPGA is described.

[0106] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned deconvolution super beamforming method for multi-beam sonar images is implemented.

[0107] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the program implements the above-mentioned deconvolution super beamforming method for multi-beam sonar images.

[0108] Through the above technical scheme, the present invention effectively improves the resolution and imaging quality of multi-beam imaging sonar in low signal-to-noise ratio and complex background noise environments, while maintaining high computing efficiency, and is suitable for a variety of underwater detection scenarios.

[0109] Compared with the prior art, the advantages of the present invention are:

[0110] 1. Improve imaging resolution

[0111] The present invention significantly improves the imaging resolution of multi-beam sonar by introducing the deconvolution hyperbeam forming (Dcv-HBF) algorithm and combining hyperbeam forming (HBF) with deconvolution processing. Compared with the traditional conventional beam forming (CBF) and HBF algorithms, Dcv-HBF can effectively reduce background noise under low signal-to-noise ratio conditions and provide clearer target images.

[0112] 2. Excellent background noise suppression capability

[0113] By optimizing the deconvolution algorithm, the present invention effectively reduces the background noise and sidelobe interference in the image, so that underwater imaging can maintain a high signal-to-noise ratio even in a noisy environment. Especially under low SNR conditions, Dcv-HBF exhibits strong noise suppression capabilities and can accurately extract target information.

[0114] 3. Accurate edge retention capability

[0115] The present invention combines HBF with the deconvolution method, which can maximize the retention of image edge information while ensuring high resolution. The improved deconvolution algorithm effectively avoids edge blur and detail loss, and is particularly suitable for scenarios with high requirements for edge information, such as underwater target detection and recognition.

[0116] 4. Adaptive beamforming and filtering optimization

[0117] The Dcv-HBF algorithm of the present invention can automatically optimize the filtering process according to the environmental noise level by adaptively selecting the order of beamforming and combining the regularization parameter with dynamic adjustment. This method ensures that the best imaging effect can be always maintained under different signal-to-noise ratios and complex environments.

[0118] 5. Strong robustness and adaptability to complex environments

[0119] In complex underwater environments, especially when there is high background noise and strong interference signals, the present invention demonstrates strong robustness through the improved HBF and deconvolution algorithm. Even under conditions of weak signals and strong noise, Dcv-HBF can still effectively distinguish targets from noise and provide reliable imaging results.

[0120] 6. Optimize computing performance and reduce computing complexity

[0121] The improved Dcv-HBF algorithm reduces the computational complexity of the algorithm by introducing an efficient computing strategy, which significantly improves the computing speed while ensuring high resolution and image quality. Especially when processing large amounts of sonar data, the optimized algorithm can respond quickly and execute efficiently.

[0122] 7. Wide application scope and cross-field application

[0123] The present invention is applicable to various types of underwater imaging tasks, especially in the fields of deep sea exploration, marine ranch monitoring, underwater target recognition, etc., which require high resolution and low noise. Its adaptive characteristics enable the method to be widely used in different underwater environments and complex noise conditions, and it has strong adaptability and universality.

[0124] Through the above advantages, the present invention has shown superior performance over traditional methods in improving underwater imaging resolution, reducing noise interference, improving image clarity and computing efficiency.

Claims

1. A deconvolution super beam forming method for multi-beam sonar images, characterized in that: The following steps are involved: S1: Collect multibeam sonar data and create a target characteristics database for algorithm development; S2: Generate point spread function for deconvolution using hyperbeamforming method; S3: Optimize the azimuth spectrum and improve the resolution through the Richardson-Lucy iterative algorithm; S4: Adjust algorithm parameters and verify the performance of the optimized Dcv-HBF algorithm through simulation and experiments; S5: Output clear target sonar images for target detection and background noise suppression analysis; The optimized Dcv-HBF algorithm generates clear sonar images of underwater targets with sharp target edges, clear details, and effectively suppressed background noise. The generated images are used for subsequent target detection, recognition, and background noise suppression analysis.

2. The deconvolution super beam forming method for multi-beam sonar images according to claim 1, characterized in that: Specifically, step 1 collects sonar data of underwater targets from multi-beam sonar equipment, including different types of underwater targets; the collected data is pre-processed to remove noise, calibrate sensor errors and perform calibration to form a target characteristic database; the database is used for subsequent algorithm development and performance verification.

3. The deconvolution super beamforming method for multi-beam sonar images according to claim 1, characterized in that: Specifically, step S2 generates a point spread function PSF for deconvolution based on the preliminary spatial spectrum obtained by optimizing the conventional beamforming CBF algorithm based on the hyperbeamforming HBF method; hyperbeamforming improves the beam directivity and target resolution and enhances the target characteristics in the spatial spectrum by dividing the receiving array into multiple sub-arrays.

4. The deconvolution super beam forming method for multi-beam sonar images according to claim 1, characterized in that: The specific method of step 3 is: use the Richardson-Lucy iterative algorithm to deconvolve the azimuth spectrum obtained by super beamforming; the algorithm iteratively corrects the blurred parts in the image, gradually restores the details of the image and improves the spatial resolution of the image; in each iteration, the algorithm uses the PSF generated by deconvolution to repair the blurred areas in the spatial spectrum, optimize the edge sharpness of the target, and improve the accuracy of target recognition.

5. The deconvolution super beam forming method for multi-beam sonar images according to claim 1, characterized in that: The specific method of step 4 is: adjust the parameters and verify the performance of the optimized Dcv-HBF algorithm through simulation and experimental data; optimize the key parameters in the algorithm by comparing the simulation data and experimental results under different environmental conditions; the key parameters include regularization parameters, number of iterations and beamforming weights.

6. A deconvolution super beamforming system for multi-beam sonar images, characterized in that: A system for implementing the deconvolution super beamforming method for multi-beam sonar images according to any one of claims 1 to 5, the system comprising: Data acquisition module: used to extract sonar image data of underwater targets from multi-beam sonar equipment; Beamforming module: Uses conventional beamforming CBF algorithm to perform preliminary beamforming and generate spatial spectrum; Hyperbeamforming module: uses the hyperbeamforming HBF algorithm to optimize the spatial spectrum and improve target resolution; Deconvolution processing module: uses Richardson-Lucy iterative algorithm to deconvolve and optimize the spatial spectrum to improve image clarity; Imaging generation module: Generates high-resolution underwater target images based on the optimized spatial spectrum for subsequent target detection and analysis.

7. The deconvolution super beamforming system for multi-beam imaging sonar according to claim 6, characterized in that: The system also includes a control and coordination module: used for coordinating the execution order of each module to ensure the smooth operation of the deconvolution super beamforming algorithm.

8. The deconvolution super beamforming system for multi-beam imaging sonar according to claim 6, characterized in that: The system also includes a data storage and management module: used to store original image data and processed image data, provide image data management and retrieval functions, and ensure the effective use of data in subsequent analysis.

9. The deconvolution super beamforming system for multi-beam imaging sonar according to claim 6, characterized in that: The system also includes a user interface module: used to provide a graphical user interface, allowing the user to input algorithm parameters, view real-time processing results and output the final underwater target image.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, which can be run on a computing device to implement the deconvolution super beamforming method for multi-beam sonar images as described in any one of claims 1 to 5.

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