Multi-beam sonar target detection and imaging method and system

Abstract

The application provides a kind of multi-beam sonar target detection and imaging method and system, comprising: data preprocessing step: through the preprocessing of original data of multi-beam acquisition, the preprocessed image data obtained is transferred to noise suppression step, and the original data sampling rate and filtering strategy are further optimized according to the feedback of noise suppression step;Noise suppression step: receiving the preprocessed image data, suppressing the background noise and sidelobe interference in the preprocessed image data, obtaining water body image;Target extraction step: obtaining water body image, extracting the significant feature scale of target, and improving the significant feature of target in water body image, completing the extraction of target information;Result analysis step: further analyzing the extracted target information, and the analysis result is transmitted to the host computer through the data interface for data back and correction, or displayed and stored in the system internally.The application can improve the detection accuracy and target recognition ability of the system in complex underwater environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-beam sonar, in particular to a multi-beam sonar target detection and imaging system based on a dynamic adaptive joint processing framework, and more particularly to a multi-beam sonar target detection and imaging method and system. BACKGROUND

[0002] Multi-beam sonar systems are widely used in underwater target detection, seabed topography mapping, and underwater structure monitoring due to their high resolution and full coverage detection capabilities. These systems emit sound waves and receive echo signals to generate water body images, which visually display the distribution and movement of targets in the underwater environment. However, existing multi-beam sonar systems still face many challenges in practical applications, such as noise interference, sidelobe effects, and multi-target recognition in complex environments.

[0003] Traditional signal processing methods often focus on the extraction and recognition of single targets, usually optimized for specific types of targets (such as bubbles or plumes). However, when faced with diverse underwater targets (such as sunken ships, pipelines, floating objects, artificial structures, etc.), the adaptability and accuracy of existing methods are insufficient. Especially in multi-beam sonar imaging, noise suppression and target feature extraction algorithms are easily disturbed by background noise and sidelobe effects when dealing with complex underwater environments, leading to decreased target recognition rate and increased false detection rate.

[0004] The invention patent with publication number CN113030981B discloses a method for adaptive adjustment of multi-beam sonar system parameters. According to the resolution model of the multi-beam sonar system, combined with the real-time motion posture and real-time test performance of the sonar installation carrier, the real-time control parameters and parameter adjustment control principles required by the multi-beam sonar in the current test environment are calculated through an algorithm, and the real-time performance and test effect of the multi-beam sonar system in the current test environment are automatically and real-time judged.

[0005] Existing multi-beam sonar systems face the following problems when faced with complex underwater environments: severe background noise interference, inaccurate target feature extraction, and insufficient adaptability of the system when dealing with diverse targets. SUMMARY

[0006] To overcome the defects in the prior art, the present application provides a multi-beam sonar target detection and imaging method and system.

[0007] According to the multi-beam sonar target detection and imaging method and system provided by the present application, the scheme is as follows:

[0008] In a first aspect, a multi-beam sonar target detection and imaging method is provided, which comprises:

[0009] Data preprocessing step: raw data is collected by multi-beam and preprocessed, and the obtained preprocessed image data is transmitted to the noise suppression step, and the raw data sampling rate and filtering strategy are further optimized according to the feedback of the noise suppression step;

[0010] Noise suppression step: receiving the preprocessed image data, suppressing the background noise and sidelobe interference in the preprocessed image data to obtain a water body image;

[0011] Target extraction step: obtaining the water body image, extracting the significant feature scale of the target, and improving the target feature saliency in the water body image to complete the extraction of target information;

[0012] Result analysis step: further analyzing the extracted target information, and transmitting the analysis result to the upper computer through the data interface for data back transmission and correction, or displaying and storing in the system.

[0013] Preferably, the data preprocessing step includes:

[0014] Step 1.1: obtaining raw data from a multi-beam detection device, and performing data format analysis and information extraction to extract Doppler velocity data, inertial navigation data and sonar image data;

[0015] Step 1.2: integrating the Doppler velocity data to obtain the velocity and displacement information of the target;

[0016] Step 1.3: analyzing the inertial navigation data to obtain the attitude information of the target, and time synchronizing the velocity and displacement data; performing coordinate conversion on the sonar image data, and then performing distance constraint filtering to eliminate noise and optimize image quality;

[0017] Step 1.4: the multi-beam data processed by the above steps is output as preprocessed image information for subsequent target recognition and analysis.

[0018] Preferably, the noise suppression step includes:

[0019] Step 2.1: obtaining preprocessed image data and performing outlier filtering on angle sequence to remove abnormal data that does not conform to the detection rule;

[0020] Step 2.2: performing sector gap intensity compensation to correct data overlap or blank area caused by detection angle coincidence or insufficient signal intensity;

[0021] Step 2.3: smoothing the noise in the preprocessed image data to reduce the noise interference in the preprocessed image data;

[0022] Step 2.4: Angle sequence adaptive threshold calibration is performed to dynamically adjust the threshold according to actual data and optimize the detection accuracy.

[0023] Step 2.5: Multi-level threshold filtering is applied to extract valid signals and suppress interference.

[0024] Step 2.6: Enter the multi-ping cumulative water bottom background estimation stage, estimate the water bottom background information through multiple ping echo signal accumulation, and perform background suppression to eliminate the influence of water bottom stray signals on image quality.

[0025] Step 2.7: After noise suppression processing, water body image information is obtained to provide input for subsequent target analysis and detection.

[0026] Preferably, the target extraction step includes:

[0027] Step 3.1: Input the water body image, perform noise scale estimation on the water body image, analyze the distribution characteristics and scale information of the noise in the image, and perform maximum feature scale estimation to extract the significant feature scale of the target, providing basic parameters for subsequent processing.

[0028] Step 3.2: Perform multi-scale local contrast enhancement on the water body image to enhance local details and contrast, improve the saliency of target features in the image, and obtain an enhanced image.

[0029] Step 3.3: Perform fuzzy clustering analysis on the enhanced image to identify potential target regions and extract stable classification clusters, and select target feature clusters with consistency and saliency from the classification clusters.

[0030] Step 3.4: Based on the extracted classification clusters, perform target feature analysis to complete the extraction of target information.

[0031] Preferably, the result analysis step includes:

[0032] Step 4.1: Perform intensity analysis, position estimation, and size calculation on the target information to obtain the basic features of the target information.

[0033] Step 4.2: Integrate and format convert the basic features of the target information.

[0034] Step 4.3: Perform multi-dimensional data comprehensive analysis by matching with features in the early warning information library to identify potential threats and abnormal situations and obtain analysis results.

[0035] Step 4.4: The analysis results are transmitted to the upper computer through the data interface for data back transmission and correction to ensure the accuracy and consistency of the data.

[0036] In a second aspect, a multi-beam sonar target detection and imaging system is provided, the system comprising:

[0037] a data preprocessing module configured to acquire raw data by multi-beam and pre-process the raw data, and to pass the pre-processed image data to a noise suppression module and further optimize the raw data sampling rate and filtering strategy according to feedback from the noise suppression module;

[0038] a noise suppression module configured to receive the pre-processed image data, suppress background noise and sidelobe interference in the pre-processed image data, and obtain a water body image;

[0039] a target extraction module configured to obtain the water body image, extract a significant feature scale of a target, and improve the target feature saliency in the water body image, thereby completing extraction of target information;

[0040] a result analysis module configured to further analyze the extracted target information, and transmit the analysis result to an upper computer via a data interface for data back transmission and correction, or display and store the analysis result internally.

[0041] Preferably, the data preprocessing module includes:

[0042] Module 1.1: obtaining raw data from a multi-beam detection device, and performing data format analysis and information extraction to extract Doppler velocity data, inertial navigation data, and sonar image data;

[0043] Module 1.2: performing integral processing on the Doppler velocity data to obtain velocity and displacement information of a target;

[0044] Module 1.3: analyzing inertial navigation data to obtain attitude information of the target, and time-synchronizing the attitude information with the velocity and displacement data; performing coordinate conversion on the sonar image data, and then performing distance constraint filtering to eliminate noise and optimize image quality;

[0045] Module 1.4: outputting the multi-beam data processed by the above modules as pre-processed image information for subsequent target recognition and analysis.

[0046] Preferably, the noise suppression module includes:

[0047] Module 2.1: obtaining pre-processed image data, and performing outlier filtering on an angle sequence to remove abnormal data that does not conform to detection rules;

[0048] Module 2.2: performing sector gap intensity compensation to correct data overlap or blank areas caused by detection angle coincidence or insufficient signal intensity;

[0049] Module 2.3: Smoothing the noise in the pre-processed image data to reduce the noise interference in the pre-processed image data;

[0050] Module 2.4: Angle sequence adaptive threshold calibration is performed to dynamically adjust the threshold according to the actual data and optimize the detection accuracy;

[0051] Module 2.5: Multi-level threshold filtering is applied to extract effective signals and suppress interference;

[0052] Module 2.6: Enter the multi-ping cumulative water bottom background estimation stage, estimate the water bottom background information through multiple ping echo signal accumulation, and perform background suppression to eliminate the influence of water bottom stray signals on image quality;

[0053] Module 2.7: After noise suppression processing, water body image information is obtained to provide input for subsequent target analysis and detection.

[0054] Preferably, the target extraction module comprises:

[0055] Module 3.1: Input water body image, estimate noise scale of water body image, analyze distribution characteristics and scale information of noise in image, and perform maximum feature scale estimation to extract significant feature scale of target and provide basic parameters for subsequent processing;

[0056] Module 3.2: Perform multi-scale local contrast enhancement on water body image, enhance local details and contrast to improve target feature saliency in image, and obtain enhanced image;

[0057] Module 3.3: Perform fuzzy clustering analysis on enhanced image, classify pixel features, identify potential target regions, and extract stable classification clusters to filter out target feature clusters with consistency and saliency;

[0058] Module 3.4: Based on the extracted classification clusters, perform target feature analysis to complete target information extraction.

[0059] Preferably, the result analysis module comprises:

[0060] Module 4.1: Perform intensity analysis, position estimation and size calculation on target information to obtain basic features of target information;

[0061] Module 4.2: Integrate and format convert basic features of target information;

[0062] Module 4.3: Perform multi-dimensional data comprehensive analysis by matching with features in early warning information library to identify potential threats and abnormal situations and obtain analysis results;

[0063] Module 4.4: The analysis result is transmitted to the upper computer through the data interface, and data is returned and corrected to ensure the accuracy and consistency of the data.

[0064] Compared with the prior art, the present application has the following beneficial effects:

[0065] 1. Strong adaptability: the present application can dynamically adjust processing parameters, adapt to different underwater environments and various types of targets, and improve detection accuracy and efficiency;

[0066] 2. High signal-to-noise ratio: the present application uses a multi-stage noise suppression strategy, and the system can effectively reduce the interference of background noise and improve the quality of effective signals;

[0067] 3. High target detection accuracy: the present application uses multi-scale segmentation and density clustering algorithm, and the system can accurately extract and identify underwater targets of different sizes and types;

[0068] 4. High system integration: the modular design of the present application makes the system flexible to expand and easy to integrate with other detection systems, suitable for a wide range of underwater detection applications.

[0069] Other beneficial effects of the present application will be described in the specific embodiments through the introduction of specific technical features and technical solutions, and those skilled in the art should be able to understand the beneficial technical effects brought by the technical features and technical solutions through the introduction of the technical features and technical solutions. BRIEF DESCRIPTION OF DRAWINGS

[0070] Other features, objects and advantages of the present application will become more apparent through reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0071] Figure 1 is the overall system block diagram of the present application;

[0072] Figure 2 is the functional block diagram of the data preprocessing module;

[0073] Figure 3 is the functional block diagram of the noise suppression module;

[0074] Figure 4 is the flowchart of the target extraction algorithm;

[0075] Figure 5 is the flowchart of the result analysis;

[0076] Figures 6a to 6e is the data processing result graph; wherein, Figure 6a is the original water body graph, Figure 6b is the gap correction water body graph, Figure 6c is the background suppression water body graph, Figure 6d is the clustering analysis water body graph,Figure 6e extracting a water body map for a target. DETAILED DESCRIPTION

[0077] The application will be described in detail below with specific examples. The following examples will help those skilled in the art to further understand the application, but do not limit the application in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the application. These are within the scope of the application.

[0078] The embodiment of the application provides a multi-beam sonar target detection and imaging method. Through a full-process optimized joint processing algorithm, noise suppression, signal enhancement and target feature extraction are integrally processed, the performance of the system in detection of different types and sizes of underwater targets is significantly improved, and the target recognition accuracy and applicability of the multi-beam sonar system are improved. Referring to the method shown in the figure, the method comprises a data preprocessing step, a noise suppression step, a target extraction step and a result analysis step, and specific contents are as follows: Figure 1

[0079] The data preprocessing step is used for preliminary processing of original data collected by the multi-beam, including data format conversion, time synchronization and preliminary filtering. The preprocessed image data is transmitted to the noise suppression step, and the original data sampling rate and filtering strategy are further optimized according to the feedback of the noise suppression step; the accuracy and consistency of the subsequent processing steps are ensured by standardizing the original data. This step includes:

[0080] Step 1.1: Obtain the original data from the multi-beam detection device, and perform data format analysis and information extraction to extract Doppler velocity data, inertial navigation data and sonar image data;

[0081] Step 1.2: Perform integral processing on the Doppler velocity data to obtain the velocity and displacement information of the target;

[0082] Step 1.3: Analyze the inertial navigation data to obtain the attitude information of the target, and synchronize the velocity and displacement data with the attitude information; perform coordinate conversion on the sonar image data, and then perform distance constraint filtering to eliminate noise and optimize image quality;

[0083] Step 1.4: The multi-beam data processed through the above steps is output as preprocessed image information for subsequent target recognition and analysis.

[0084] ​Noise suppression step: receiving the pre-processed image data, suppressing the background noise and sidelobe interference in the pre-processed image data to obtain the water body image; this step can automatically adjust the filtering parameters according to the real-time changes of the environmental noise, ensuring a high signal-to-noise ratio in different environments. By adopting a multi-level filtering strategy, the global noise is first preliminarily suppressed, and then the effective signal intensity is improved through a local enhancement algorithm, thereby enhancing the distinguishability of the target features. This step specifically includes:

[0085] Step 2.1: Obtain the pre-processed image data, and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection rule;

[0086] Step 2.2: Perform sector gap intensity compensation to correct the data overlap or blank area caused by the detection angle coincidence or insufficient signal intensity;

[0087] Step 2.3: Smooth the noise in the pre-processed image data to reduce the noise interference in the pre-processed image data;

[0088] Step 2.4: Perform angle sequence adaptive threshold calibration to dynamically adjust the threshold according to the actual data and optimize the detection accuracy;

[0089] Step 2.5: Apply multi-level threshold filtering to extract the effective signal and suppress interference;

[0090] Step 2.6: Enter the multi-ping cumulative water bottom background estimation stage, estimate the water bottom background information by accumulating multiple ping echo signals, and perform background suppression to eliminate the influence of water bottom stray signals on image quality;

[0091] Step 2.7: After noise suppression processing, obtain the water body image information to provide input for subsequent target analysis and detection.

[0092] Target extraction step: obtain the water body image, extract the significant feature scale of the target, and improve the saliency of the target features in the water body image to complete the extraction of target information. This step specifically includes:

[0093] Step 3.1: Input the water body image, perform noise scale estimation on the water body image, analyze the distribution characteristics and scale information of the noise in the image, and perform maximum feature scale estimation to extract the significant feature scale of the target, providing basic parameters for subsequent processing;

[0094] Step 3.2: Perform multi-scale local contrast enhancement on the water body image to improve the saliency of the target features in the image by enhancing local details and contrast, obtaining an enhanced image;

[0095] Step 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target regions by classifying pixel features, and extract stable classification clusters to filter out consistent and significant target feature clusters.

[0096] Step 3.4: Based on the extracted classification clusters, perform target feature analysis to complete the extraction of target information.

[0097] Result analysis step: Further analyze the extracted target information, including target classification, position estimation, and size calculation. The analysis results are transmitted to the host computer through the data interface for data back transmission and correction, or displayed and stored internally for subsequent use. This step supports multiple data output formats, can seamlessly integrate with existing underwater detection systems, and provides real-time monitoring and alarm functions, suitable for various application scenarios such as underwater target detection, environmental monitoring, and resource exploration. This step specifically includes:

[0098] Step 4.1: Perform intensity analysis, position estimation, and size calculation on the target information to obtain the basic features of the target information;

[0099] Step 4.2: Integrate and format convert the basic features of the target information;

[0100] Step 4.3: Perform multi-dimensional data comprehensive analysis by matching with the features in the early warning information library to identify potential threats and abnormal situations, and obtain the analysis results;

[0101] Step 4.4: The analysis results are transmitted to the host computer through the data interface for data back transmission and correction to ensure the accuracy and consistency of the data.

[0102] The present application also provides a multi-beam sonar target detection and imaging system, which can be realized by executing the process steps of the multi-beam sonar target detection and imaging method, i.e. the multi-beam sonar target detection and imaging method can be understood by those skilled in the art as the preferred embodiment of the multi-beam sonar target detection and imaging system.

[0103] The system includes a data preprocessing module, a noise suppression module, a target extraction module, and a result analysis module. The system can adapt to complex underwater environments in real time by dynamically adjusting algorithm parameters, improving the accuracy and efficiency of target recognition. The specific content is as follows:

[0104] Data preprocessing module: used for preliminary processing of raw data collected by multi-beam acquisition, including data format conversion, time synchronization and preliminary filtering. The preprocessed image data is passed to the noise suppression module, and the raw data sampling rate and filtering strategy are further optimized according to the feedback of the noise suppression module; through standardization processing of the raw data, the accuracy and consistency of the subsequent processing module are ensured. This module includes:

[0105] Module 1.1: raw data is obtained from the multi-beam detection device, and data format analysis and information extraction are performed, extracting Doppler velocity data, inertial navigation data and sonar image data;

[0106] Module 1.2: the Doppler velocity data is integrated to obtain the velocity and displacement information of the target;

[0107] Module 1.3: analyze the inertial navigation data to obtain the attitude information of the target, and synchronize it with the velocity and displacement data; perform coordinate conversion on the sonar image data, and then perform distance constraint filtering to eliminate noise and optimize image quality;

[0108] Module 1.4: the multi-beam data processed by the above modules is output as preprocessed image information for subsequent target recognition and analysis.

[0109] Noise suppression module: receives the preprocessed image data, suppresses the background noise and sidelobe interference in the preprocessed image data, and obtains the water body image; this module can automatically adjust the filtering parameters according to the real-time changes of environmental noise, ensuring a high signal-to-noise ratio in different environments. By adopting a multi-level filtering strategy, the global noise is first preliminarily suppressed, and then the local enhancement algorithm is used to improve the strength of the effective signal, thereby enhancing the distinguishability of the target features. This module specifically includes:

[0110] Module 2.1: obtain the preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that do not conform to the detection rules;

[0111] Module 2.2: perform sector gap intensity compensation to correct data overlap or blank areas caused by detection angle coincidence or insufficient signal strength;

[0112] Module 2.3: smooth the noise in the preprocessed image data to reduce noise interference in the preprocessed image data;

[0113] Module 2.4: perform adaptive threshold calibration on the angle sequence, dynamically adjust the threshold according to the actual data, and optimize the detection accuracy;

[0114] Module 2.5: apply multi-level threshold filtering to extract effective signals and suppress interference;

[0115] Module 2.6: Enter the multi-ping cumulative water bottom background estimation phase, estimate the water bottom background information through multi-ping echo signal accumulation, and perform background suppression to eliminate the influence of water bottom stray signals on image quality;

[0116] Module 2.7: After noise suppression processing, obtain water body image information to provide input for subsequent target analysis and detection.

[0117] Target extraction module: Obtain water body image, extract target significant feature scale, and improve target feature saliency in water body image to complete target information extraction. This module specifically includes:

[0118] Module 3.1: Input water body image, perform noise scale estimation on water body image, analyze noise distribution characteristics and scale information in image, and perform maximum feature scale estimation to extract target significant feature scale to provide basic parameters for subsequent processing;

[0119] Module 3.2: Perform multi-scale local contrast enhancement on water body image, enhance local details and contrast to improve target feature saliency in image to obtain enhanced image;

[0120] Module 3.3: Perform fuzzy clustering analysis on enhanced image, identify potential target regions through pixel feature classification, and extract stable classification clusters to filter out target feature clusters with consistency and saliency;

[0121] Module 3.4: Based on extracted classification clusters, perform target feature analysis to complete target information extraction.

[0122] Result analysis module: Further analyze extracted target information, including target classification, position estimation, and size calculation. Analysis results are transmitted to host computer through data interface for data back transmission and correction, or displayed and stored internally for subsequent use. This module supports multiple data output formats, can be seamlessly integrated with existing underwater detection systems, and provides real-time monitoring and alarm functions, suitable for various application scenarios such as underwater target detection, environmental monitoring, and resource exploration. This module specifically includes:

[0123] Module 4.1: Perform intensity analysis, position estimation, and size calculation on target information to obtain basic features of target information;

[0124] Module 4.2: Integrate and format convert basic features of target information;

[0125] Module 4.3: Perform multi-dimensional data comprehensive analysis by matching with features in early warning information library to identify potential threats and abnormal situations to obtain analysis results;

[0126] Module 4.4: The analysis results are transmitted to the host computer through the data interface for data return and correction to ensure the accuracy and consistency of the data.

[0127] Next, the application will be more specific description.

[0128] The application provides a multi-beam sonar target detection and imaging system, which can be optimized through the feedback mechanism and parameter dynamic adjustment among modules to realize joint processing, integrates a filtering algorithm based on environmental noise self-learning to realize noise suppression, signal enhancement and multi-level target feature fusion and classification model target feature extraction, so as to improve the detection accuracy and target recognition ability of the system in complex underwater environment. The system framework is as shown in Figure 1 The core of the system is to form an adaptive overall system through dynamic coupling and feedback mechanism to improve the accuracy and efficiency of underwater target detection. The system includes a data preprocessing module, a noise suppression module, a target extraction module and a result analysis module.

[0129] The data preprocessing module not only transmits data to the noise suppression module, but also further optimizes the data sampling rate and filtering strategy according to the feedback of the noise suppression module;

[0130] The target extraction module not only depends on the processing results of the noise suppression module, but also can adjust the filtering level or adaptive parameters of the noise suppression module through the feedback mechanism to enhance the recognizability of the target;

[0131] The result analysis module participates in the whole extraction and processing process, not only returns and corrects the data, but also feeds back and affects the selection and adjustment of the target extraction algorithm through classification and accuracy analysis to improve the accuracy of the system. The functions and flowcharts of the modules are described as follows.

[0132] Data preprocessing module:

[0133] The data preprocessing module is responsible for the preliminary processing of the collected multi-beam data to ensure the consistency and accuracy of the data. The module introduces adaptive data processing strategy to adapt to the changes of different underwater environments, including data format conversion, time synchronization and preliminary filtering steps. The data preprocessing flowchart is as shown in Figure 2As shown, first, the system obtains raw data from the multi-beam detection device and performs data format parsing and information extraction, extracting Doppler velocity data, inertial navigation data, and sonar image data. Next, the Doppler velocity data is integrated to obtain the target's velocity and displacement information. Then, the inertial navigation data is parsed to obtain the target's attitude information, which is time-synchronized with the velocity and displacement data. The sonar image data is converted in coordinates, and then distance-constrained filtering is performed to eliminate noise and optimize image quality. Finally, the data processed by the above modules is output as preprocessed image data for subsequent target recognition and analysis. Through these modules, the system can convert raw data into a standard format, laying the foundation for subsequent noise suppression and target extraction.

[0134] Noise suppression module:

[0135] The specific process of noise suppression is as shown in Figure 3 First, the preprocessed image data is obtained, and the angle sequence is subjected to outlier filtering to remove abnormal data that does not conform to the detection rules. Next, sector gap intensity compensation is performed to correct data overlap or blank areas caused by overlapping detection angles or insufficient signal strength. Then, noise in the preprocessed image data is smoothed to reduce noise interference in the preprocessed image data. Next, the angle sequence adaptive threshold calibration is performed, dynamically adjusting the threshold according to the actual data to optimize the detection accuracy. Then, multi-level threshold filtering is applied to further extract valid signals and suppress unnecessary interference. The multi-ping cumulative seabed background estimation stage is entered, the seabed background information is estimated through multiple ping echo signal accumulation, and background suppression is performed to eliminate the influence of seabed stray signals on image quality. Finally, after noise suppression processing, a clear water image is obtained, providing reliable input for subsequent target analysis and detection. Through the system's signal processing and noise control, the entire process significantly improves the image quality, ensuring the accuracy and reliability of the data, and providing solid data support for subsequent research. By dynamically adjusting the filtering parameters, this module can adapt to different underwater environments and effectively suppress noise interference.

[0136] Target extraction module:

[0137] The specific process of target extraction is as shown in Figure 4As shown, the process begins with noise scale estimation of the input water image. This involves analyzing the distribution characteristics and scale information of noise within the image, along with maximum feature scale estimation to extract the salient feature scale of the target, providing fundamental parameters for subsequent processing. Next, multi-scale local contrast enhancement is performed on the image. By enhancing local details and contrast, the salience of target features in the image is improved, resulting in an enhanced image. Subsequently, fuzzy clustering analysis is applied to the enhanced image. By classifying pixel features, potential target regions are identified, and stable classification clusters are extracted. From these, target feature clusters with consistency and saliency are selected. Finally, based on the extracted stable classification clusters, target feature analysis is performed to extract key information about the target, including shape, size, and intensity, completing the target extraction process. This workflow, through accurate noise estimation, scale analysis, multi-scale enhancement, and clustering analysis, achieves effective target extraction, providing high-quality input data for subsequent target recognition and classification. This module can handle various complex underwater targets, including shipwrecks, pipes, and floating objects.

[0138] Results Analysis Module:

[0139] The results analysis process is as follows: Figure 5 As shown in the diagram, the results analysis module begins by receiving the extracted target information. It first performs intensity analysis, location estimation, and size calculation to obtain the target's basic characteristics. Then, the module integrates and converts this data for subsequent processing and analysis. Next, it performs multi-dimensional data analysis by matching features with those in the early warning information database to identify potential threats and anomalies. The analysis results are displayed in real-time on the system interface and trigger alarms to promptly notify relevant personnel. Finally, the analysis results are transmitted to the host computer via a data interface for data feedback and correction to ensure data accuracy and consistency. This process, through efficient data processing, feature matching, and comprehensive analysis, achieves accurate target identification and tracking, providing timely and reliable support for decision-making and response. This module supports multiple data output formats, suitable for various application scenarios, such as underwater environmental monitoring and resource exploration.

[0140] Through the coupling and feedback mechanisms between the modules, the system is able to extract high-precision underwater target information. The processing results are as follows: Figures 6a to 6e As shown, even with complex background noise in the original water map, the target was successfully extracted through gap correction, background suppression, and cluster analysis. This not only successfully eliminated background noise but also ensured the integrity and continuity of the target. Through the comprehensive processing of this system, the extracted target data has higher accuracy and reliability, making it suitable for complex underwater exploration missions.

[0141] The embodiment of the present application provides a kind of multi-beam sonar target detection and imaging method and system, through full-process optimization algorithm and joint processing strategy, multiple processing steps such as noise suppression, signal enhancement and target feature extraction are integrated, not only improve the precision of detection and the reliability of target identification, can also adapt to complex underwater environment, while can significantly improve the precision and efficiency of multi-beam detection, the system has wide application prospect in underwater environment monitoring, resource exploration and other fields.

[0142] Those skilled in the art know that, in addition to implementing the system provided by the present application and each device, module, unit thereof in a pure computer readable program code manner, the same function can be realized by logically programming method steps to make the system provided by the present application and each device, module, unit thereof in the form of logic gate, switch, application specific integrated circuit, programmable logic controller and embedded microcontroller. Therefore, the system provided by the present application and each device, module, unit thereof can be considered as a hardware component, and the devices, modules and units included therein for realizing various functions can also be considered as structures within the hardware component; the devices, modules and units for realizing various functions can also be considered as both software modules realizing methods and structures within the hardware component.

[0143] The specific embodiments of the present application are described above. It needs to be understood that the present application is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essential content of the present application. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A multi-beam sonar target detection and imaging method, characterized in that, include: Data preprocessing step: Acquire raw data through multi-beam acquisition and preprocess it. Pass the acquired preprocessed image data to the noise suppression step. Based on the feedback from the noise suppression step, further optimize the raw data sampling rate and filtering strategy. Noise suppression step: Receive the preprocessed image data, suppress background noise and sidelobe interference in the preprocessed image data, and obtain a water body image; Target extraction steps: Acquire water body images, extract the salient feature scale of the target, and improve the salientity of target features in the water body images to complete the extraction of target information; Results analysis steps: Further analyze the extracted target information, and transmit the analysis results to the host computer through the data interface for data feedback and correction, or display and store them within the system; The data preprocessing step includes: Step 1.1: Obtain raw data from the multibeam detector and perform data format parsing and information extraction to extract Doppler velocity data, inertial navigation data and sonar image data; Step 1.2: Integrate the Doppler velocity data to obtain the target's velocity and displacement information; Step 1.3: Parse the inertial navigation data to obtain the target's attitude information and synchronize it with the velocity and displacement data in time; perform coordinate transformation on the sonar image data, and then perform range constraint filtering to eliminate noise and optimize image quality; Step 1.4: The multibeam data processed in the above steps is output as preprocessed image information for subsequent target recognition and analysis; The noise suppression step includes: Step 2.1: Acquire preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that do not conform to the detection pattern; Step 2.2: Perform sector gap strength compensation to correct data overlap or blank areas caused by overlapping detection angles or insufficient signal strength; Step 2.3: Smooth the noise in the preprocessed image data to reduce noise interference. Step 2.4: Perform adaptive threshold calibration of the angle sequence, dynamically adjust the threshold according to the actual data, and optimize the detection accuracy; Step 2.5: Apply multi-level threshold filtering to extract the effective signal and suppress interference; Step 2.6: Enter the multi-ping accumulation underwater background estimation stage. By accumulating the echo signals from multiple pings, the underwater background information is estimated, and background suppression is performed to eliminate the influence of underwater stray signals on image quality. Step 2.7: After noise suppression processing, water body image information is obtained, providing input for subsequent target analysis and detection; The target extraction steps include: Step 3.1: Input a water body image, perform noise scale estimation on the water body image, analyze the distribution characteristics and scale information of noise in the image, and at the same time perform maximum feature scale estimation to extract the salient feature scale of the target, providing basic parameters for subsequent processing; Step 3.2: Perform multi-scale local contrast enhancement on the water body image. By enhancing local details and contrast, the salience of target features in the image is improved, resulting in an enhanced image. Step 3.3: Perform fuzzy clustering analysis on the enhanced image. By classifying pixel features, identify potential target regions and extract stable classification clusters. From these, select target feature clusters with consistency and saliency. Step 3.4: Based on the extracted classification clusters, perform target feature analysis to complete the extraction of target information; The result analysis steps include: Step 4.1: Perform intensity analysis, location estimation, and size calculation on the target information to obtain the basic characteristics of the target information; Step 4.2: Integrate and convert the basic features of the target information; Step 4.3: By matching features with those in the early warning information database, conduct multi-dimensional data comprehensive analysis to identify potential threats and anomalies, and obtain the analysis results; Step 4.4: The analysis results are transmitted to the host computer via the data interface for data feedback and correction to ensure the accuracy and consistency of the data.

2. A multi-beam sonar target detection and imaging system, characterized in that, include: Data preprocessing module: Acquires raw data through multi-beam acquisition and preprocesses it, then transmits the acquired preprocessed image data to the noise suppression module, and further optimizes the raw data sampling rate and filtering strategy based on the feedback from the noise suppression module; Noise suppression module: Receives the preprocessed image data, suppresses background noise and sidelobe interference in the preprocessed image data, and obtains a water body image; Target extraction module: Acquires water body images, extracts the salient feature scale of targets, and improves the salientity of target features in water body images to complete the extraction of target information; Results Analysis Module: Further analyzes the extracted target information. The analysis results are transmitted to the host computer via the data interface for data feedback and correction, or displayed and stored within the system. The preprocessing in the data preprocessing module includes: Module 1.1: Acquire raw data from the multibeam detector, perform data format parsing and information extraction, and extract Doppler velocity data, inertial navigation data and sonar image data; Module 1.2: Integrates the Doppler velocity data to obtain the target's velocity and displacement information; Module 1.3: Parse inertial navigation data to obtain target attitude information and synchronize it with velocity and displacement data in time; perform coordinate transformation on sonar image data, followed by range-constrained filtering to eliminate noise and optimize image quality; Module 1.4: The multibeam data processed by the above modules is output as preprocessed image information for subsequent target recognition and analysis; The noise suppression module includes: Module 2.1: Acquire preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection pattern; Module 2.2: Perform sector gap strength compensation to correct data overlap or blank areas caused by overlapping detection angles or insufficient signal strength; Module 2.3: Smooths noise in the preprocessed image data to reduce noise interference. Module 2.4: Perform adaptive threshold calibration of angle sequences, dynamically adjust the threshold based on actual data, and optimize detection accuracy; Module 2.5: Apply multi-level threshold filtering to extract the effective signal and suppress interference; Module 2.6: Enters the multi-ping accumulation underwater background estimation stage. By accumulating multiple ping echo signals, underwater background information is estimated, and background suppression is performed to eliminate the impact of underwater stray signals on image quality. Module 2.7: After noise suppression processing, water body image information is obtained, providing input for subsequent target analysis and detection; The target extraction module includes: Module 3.1: Input a water body image, perform noise scale estimation on the water body image, analyze the distribution characteristics and scale information of noise in the image, and simultaneously perform maximum feature scale estimation to extract the salient feature scale of the target, providing basic parameters for subsequent processing; Module 3.2: Perform multi-scale local contrast enhancement on water images. By enhancing local details and contrast, the salience of target features in the image is improved, resulting in an enhanced image. Module 3.3: Perform fuzzy clustering analysis on the enhanced image. By classifying pixel features, identify potential target regions and extract stable classification clusters. From these, select target feature clusters with consistency and saliency. Module 3.4: Based on the extracted classification clusters, perform target feature analysis to complete the extraction of target information; The result analysis module includes: Module 4.1: Perform intensity analysis, location estimation, and size calculation on target information to obtain the basic characteristics of the target information; Module 4.2: Integrating and formatting the basic characteristics of the target information; Module 4.3: By matching features with those in the early warning information database, multi-dimensional data comprehensive analysis is performed to identify potential threats and abnormal situations, and to obtain analysis results; Module 4.4: The analysis results are transmitted to the host computer through the data interface for data feedback and correction to ensure the accuracy and consistency of the data.

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