Multi-beam sonar target detection and imaging method and system

Abstract

The invention provides a multi-beam sonar target detection and imaging method and system, and the method comprises the steps: a data preprocessing step: carrying out the preprocessing of original data through multi-beam collection, transmitting the obtained preprocessed image data to a noise suppression step, and further optimizing the sampling rate and filtering strategy of the original data according to the feedback of the noise suppression step; a noise suppression step: receiving the preprocessed image data, and suppressing background noise and sidelobe interference in the preprocessed image data to obtain a water body image; a target extraction step: obtaining a water body image, extracting a significant feature scale of a target, improving target feature significance in the water body image, and completing extraction of target information; and a result analysis step: further analyzing the extracted target information, and transmitting an analysis result to an upper computer through a data interface for data return and correction, or displaying and storing in the system. According to the invention, the detection precision and target identification capability of the system in a complex underwater environment can be improved.

Description

Technical Field

[0001] The present invention 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 in particular to a multi-beam sonar target detection and imaging method and system. Background Art

[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 generate water images by transmitting sound waves and receiving echo signals to intuitively display the distribution and movement of targets in underwater environments. 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 tend to focus on the extraction and recognition of a single target, and are usually optimized for specific types of targets (such as bubbles or plumes). However, when faced with a variety of 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 interfered by background noise and sidelobe effects when dealing with complex underwater environments, resulting in a decrease in target recognition rate and an increase in false detection rate.

[0004] The invention patent with publication number CN113030981B discloses a method for adaptively adjusting the parameters of a multi-beam sonar system. Based on the resolution model of the multi-beam sonar system and combined with the real-time motion posture and real-time test performance of the sonar mounting carrier, the real-time control parameters and parameter adjustment control principles required by the multi-beam sonar in the current test environment are inferred 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 determined.

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

[0006] In view of the defects in the prior art, the present invention provides a multi-beam sonar target detection and imaging method and system.

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

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

[0009] Data preprocessing step: collect raw data through multi-beam and preprocess them, pass the acquired preprocessed image data to the noise suppression step, and further optimize the raw data sampling rate and filtering strategy based on the feedback of the noise suppression step;

[0010] Noise suppression step: receiving the pre-processed image data, suppressing background noise and sidelobe interference in the pre-processed image data, and obtaining a water body image;

[0011] Target extraction step: obtain water body images, extract the significant feature scale of the target, and improve the saliency of the target features in the water body images to complete the extraction of target information;

[0012] Result analysis step: 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.

[0013] Preferably, the data preprocessing step includes:

[0014] Step 1.1: Obtain raw data from the multi-beam detection equipment, perform data format analysis and information extraction, and extract Doppler velocity data, inertial navigation data, and sonar image data;

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

[0016] Step 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality;

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

[0018] Preferably, the noise suppression step comprises:

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

[0020] Step 2.2: Perform sector gap intensity compensation to correct data overlap or blank areas caused by detection angle overlap or insufficient signal strength;

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

[0023] Step 2.5: Apply multi-level threshold filtering to extract valid signals and suppress interference;

[0024] Step 2.6: Enter the multi-ping accumulation bottom background estimation stage, estimate the bottom background information by accumulating multiple ping echo signals, and perform background suppression to eliminate the impact of 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 comprises:

[0027] Step 3.1: Input the water body image, estimate the noise scale of the water body image, analyze the distribution characteristics and scale information of the noise in the image, and estimate the maximum characteristic scale at the same time, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing;

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

[0029] Step 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out;

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

[0031] Preferably, the result analysis step comprises:

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

[0033] Step 4.2: Integrate and convert the basic features of the target information into a new format;

[0034] Step 4.3: By matching the features in the early warning information database, a comprehensive multi-dimensional data analysis is performed to identify potential threats and abnormal situations and obtain analysis results;

[0035] Step 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.

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

[0037] Data preprocessing module: collects raw data through multi-beam and preprocesses it, passes 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;

[0038] Noise suppression module: receiving the pre-processed image data, suppressing background noise and sidelobe interference in the pre-processed image data, and obtaining a water body image;

[0039] Target extraction module: obtains water body images, extracts the significant feature scale of the target, and improves the saliency of the target features in the water body image to complete the extraction of target information;

[0040] Result analysis module: further analyzes the extracted target information, and transmits the analysis results to the host computer through the data interface for data feedback and correction, or displays and stores them within the system.

[0041] Preferably, the preprocessing in the data preprocessing module includes:

[0042] Module 1.1: Obtain raw data from multi-beam detection equipment, perform data format analysis and information extraction, and extract Doppler velocity data, inertial navigation data, and sonar image data;

[0043] Module 1.2: Integrate the Doppler velocity data to obtain the target's velocity and displacement information;

[0044] Module 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality;

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

[0046] Preferably, the noise suppression module comprises:

[0047] Module 2.1: Obtain preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection rules;

[0048] Module 2.2: Perform sector gap strength compensation to correct data overlap or blank areas caused by overlapping detection angles or insufficient signal strength;

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

[0050] Module 2.4: Perform angle sequence adaptive threshold calibration, dynamically adjust the threshold according to actual data, and optimize detection accuracy;

[0051] Module 2.5: Apply multi-level threshold filtering to extract valid signals and suppress interference;

[0052] Module 2.6: Enter the multi-ping accumulation underwater background estimation stage, estimate the underwater background information through the accumulation of multiple ping echo signals, and perform background suppression to eliminate the impact of underwater 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 images, estimate the noise scale of water body images, analyze the distribution characteristics and scale information of noise in the images, estimate the maximum characteristic scale, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing;

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

[0057] Module 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out;

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

[0059] Preferably, the result analysis module includes:

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

[0061] Module 4.2: Integrate and convert the basic features of target information into different formats;

[0062] Module 4.3: By matching the features in the early warning information database, conduct a comprehensive analysis of multi-dimensional data, identify potential threats and abnormal situations, and obtain analysis results;

[0063] 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.

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

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

[0066] 2. High signal-to-noise ratio: The present invention adopts a multi-level 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 invention adopts multi-scale segmentation and density clustering algorithms, 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 invention enables the system to be flexibly expanded and easily integrated with other detection systems, making it suitable for a wide range of underwater detection applications.

[0069] Other beneficial effects of the present invention will be explained in the specific implementation manner through the introduction of specific technical features and technical solutions. Through the introduction of these technical features and technical solutions, those skilled in the art should be able to understand the beneficial technical effects brought about by the technical features and technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0074] Figure 4 Extract algorithm flow chart for target;

[0075] Figure 5 Flowchart for result analysis;

[0076] Figure 6a~6e is the data processing result diagram; among them, Figure 6a is the original water body map, Figure 6b To correct the water map for gaps, Figure 6c is the background suppressed water map, Figure 6d To cluster the water body map, Figure 6e Extract water body map for the target. DETAILED DESCRIPTION

[0077] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0078] The embodiment of the present invention provides a multi-beam sonar target detection and imaging method, which integrates noise suppression, signal enhancement and target feature extraction through a joint processing algorithm optimized throughout the entire process, significantly improves the system's performance in detecting underwater targets of different types and sizes, and improves the target recognition accuracy and applicability of the multi-beam sonar system. Figure 1 As shown, the method includes: data preprocessing step, noise suppression step, target extraction step and result analysis step, and the specific contents are as follows:

[0079] Data preprocessing step: used to perform preliminary processing on the raw data collected by multi-beam, including data format conversion, time synchronization and preliminary filtering. The preprocessed image data is passed to the noise suppression step, and the raw data sampling rate and filtering strategy are further optimized based on the feedback from the noise suppression step; the raw data is standardized to ensure the accuracy and consistency of subsequent processing steps. This step includes:

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

[0081] Step 1.2: Integrate the Doppler velocity data to obtain the velocity and displacement information of the target;

[0082] Step 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality;

[0083] Step 1.4: The multi-beam data processed in the above steps is output as pre-processed 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, and obtaining a water body image; this step can automatically adjust the filtering parameters according to the real-time changes in the environmental noise to ensure a high signal-to-noise ratio in different environments. By adopting a multi-stage filtering strategy, the global noise is first initially suppressed, and then the strength of the effective signal is increased through a local enhancement algorithm, thereby enhancing the recognizability of the target features. This step specifically includes:

[0085] Step 2.1: Obtain preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection law;

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

[0087] Step 2.3: Smoothing the noise in the preprocessed image data to reduce the noise interference in the preprocessed image data;

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

[0089] Step 2.5: Apply multi-level threshold filtering to extract valid signals and suppress interference;

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

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

[0092] Target extraction step: obtain water body images, 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, estimate the noise scale of the water body image, analyze the distribution characteristics and scale information of the noise in the image, and estimate the maximum characteristic scale at the same time, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing;

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

[0095] Step 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out;

[0096] Step 3.4: Based on the extracted classification clusters, target feature analysis is performed 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 feedback and correction, or displayed and stored within the system for subsequent use. This step supports multiple data output formats, can be seamlessly integrated with existing underwater detection systems, and provides real-time monitoring and alarm functions. It is suitable for a variety of 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 characteristics of the target information;

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

[0100] Step 4.3: By matching the features in the early warning information database, a comprehensive multi-dimensional data analysis is performed to identify potential threats and abnormal situations and obtain analysis results;

[0101] Step 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.

[0102] The present invention also provides a multi-beam sonar target detection and imaging system, which can be implemented by executing the process steps of the multi-beam sonar target detection and imaging method, that is, those skilled in the art can understand the multi-beam sonar target detection and imaging method as a preferred implementation of the multi-beam sonar target detection and imaging system.

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

[0104] Data preprocessing module: used to perform preliminary processing on the raw data collected by multi-beam, 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; the accuracy and consistency of the subsequent processing modules are ensured by standardizing the raw data. This module includes:

[0105] Module 1.1: Obtain raw data from multi-beam detection equipment, perform data format analysis and information extraction, and extract Doppler velocity data, inertial navigation data, and sonar image data;

[0106] Module 1.2: Integrate the Doppler velocity data to obtain the target's velocity and displacement information;

[0107] Module 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality;

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

[0109] Noise suppression module: Receive the pre-processed image data, suppress the background noise and sidelobe interference in the pre-processed image data, and obtain a water body image; this module can automatically adjust the filtering parameters according to the real-time changes of the environmental noise to ensure a high signal-to-noise ratio in different environments. By adopting a multi-stage filtering strategy, the global noise is first initially suppressed, and then the strength of the effective signal is increased through a local enhancement algorithm, thereby enhancing the recognizability of the target features. This module specifically includes:

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

[0111] Module 2.2: Perform sector gap strength compensation to correct data overlap or blank areas caused by overlapping detection angles or insufficient signal strength;

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

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

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

[0115] Module 2.6: Enter the multi-ping accumulation underwater background estimation stage, estimate the underwater background information through the accumulation of multiple ping echo signals, and perform background suppression to eliminate the impact of underwater stray signals on image quality;

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

[0117] Target extraction module: obtain water body images, 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 module specifically includes:

[0118] Module 3.1: Input water body images, estimate the noise scale of water body images, analyze the distribution characteristics and scale information of noise in the images, estimate the maximum characteristic scale, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing;

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

[0120] Module 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out;

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

[0122] Result analysis module: further analyzes 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 feedback and correction, or displayed and stored within the system 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. It is suitable for a variety of application scenarios, such as underwater target detection, environmental monitoring and resource exploration. This module specifically includes:

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

[0124] Module 4.2: Integrate and convert the basic features of target information into different formats;

[0125] Module 4.3: By matching the features in the early warning information database, conduct a comprehensive analysis of multi-dimensional data, identify potential threats and abnormal situations, and obtain analysis results;

[0126] 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.

[0127] Next, the present invention will be described in more detail.

[0128] The present invention provides a multi-beam sonar target detection and imaging system, which can optimize the whole process and realize joint processing through the feedback mechanism and dynamic adjustment of parameters between modules, and integrates the filtering algorithm based on environmental noise self-learning to realize noise suppression, signal enhancement and target feature extraction of multi-level target feature fusion and classification model, so as to improve the detection accuracy and target recognition ability of the system in complex underwater environments. The system framework is as follows Figure 1 As shown, 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 passes the data to the noise suppression module, but also further optimizes the data sampling rate and filtering strategy based on the feedback from the noise suppression module;

[0130] The target extraction module not only relies 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 identifiability of the target;

[0131] The result analysis module participates in the entire extraction and processing process. It not only transmits and corrects the data, but also provides feedback and influences the selection and adjustment of the target extraction algorithm through classification and precision analysis to improve the accuracy of the system. The functions and flow charts of each module are described below.

[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. This module introduces an adaptive data processing strategy to adapt to the changes in different underwater environments, including data format conversion, time synchronization, and preliminary filtering. The data preprocessing process is as follows: Figure 2As shown in the figure, first, the system obtains raw data from the multi-beam detection device, and performs data format analysis and information extraction to extract 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, and it is time-synchronized with the velocity and displacement data. The sonar image data is coordinate-converted, 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 pre-processed image data for subsequent target identification 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 follows: Figure 3 As shown in the figure, first, the preprocessed image data is obtained, and the angle sequence is filtered for outliers to remove abnormal data that does not conform to the detection law. Then, the sector gap intensity compensation is performed to correct the data overlap or blank area caused by the overlap of the detection angle or insufficient signal strength. Subsequently, the noise in the preprocessed image data is smoothed to reduce the noise interference in the preprocessed image data. Next, the angle sequence adaptive threshold calibration is performed, and the threshold is dynamically adjusted according to the actual data to optimize the detection accuracy. Then, multi-level threshold filtering is applied to further extract the effective signal and suppress unnecessary interference. Entering the multi-ping accumulated bottom background estimation stage, the bottom background information is estimated by accumulating multiple ping echo signals, and background suppression is performed to eliminate the influence of bottom stray signals on image quality. Finally, after noise suppression processing, a clear water body image is obtained, which provides reliable input for subsequent target analysis and detection. The entire process significantly improves the image quality through systematic signal processing and noise control, ensures the accuracy and reliability of the data, and provides solid data support for subsequent research. By dynamically adjusting the filtering parameters, the 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 follows: Figure 4As shown in the figure. First, the noise scale of the input water body image is estimated, the distribution characteristics and scale information of the noise in the image are analyzed, and the maximum characteristic scale is estimated at the same time, and the significant characteristic scale of the target is extracted to provide basic parameters for subsequent processing. Then, the image is subjected to multi-scale local contrast enhancement, and the target feature saliency in the image is improved by enhancing local details and contrast to obtain an enhanced image. Subsequently, the enhanced image is subjected to fuzzy clustering analysis, and the potential target area is identified by classifying pixel features, and stable classification clusters are extracted, from which target feature clusters with consistency and significance are screened out. Finally, based on the extracted stable classification clusters, target feature analysis is performed to extract the key information of the target, including morphology, size, intensity, etc., to complete the target extraction process. This process achieves effective target extraction through accurate noise estimation, scale analysis, multi-scale enhancement and cluster analysis, and provides high-quality input data for subsequent target recognition and classification. This module can cope with various complex underwater targets, including sunken ships, pipelines, floating objects, etc.

[0138] Result analysis module:

[0139] The result analysis process is as follows Figure 5 As shown. The result analysis module starts with receiving the extracted target information, first performing intensity analysis, position estimation and size calculation on the target information to obtain the basic characteristics of the target. Subsequently, the module integrates and converts the data for subsequent processing and analysis. Next, by matching the features in the early warning information library, a comprehensive analysis of multi-dimensional data is performed to identify potential threats and abnormal situations. The analysis results will be displayed in real time on the system interface, and an alarm will be triggered to notify relevant personnel in a timely manner. Finally, 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. This process achieves accurate identification and tracking of targets through efficient data processing, feature matching and comprehensive analysis, providing timely and reliable support for decision-making and response. The module supports multiple data output formats and is suitable for different application scenarios, such as underwater environmental monitoring and resource exploration.

[0140] Through the coupling and feedback mechanism between modules, the system can extract high-precision underwater target information. Figure 6a~6e As shown in the figure. In the case of complex background noise in the original water body image, the target was successfully extracted through gap correction, background suppression, cluster analysis, etc., which not only successfully eliminated the 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, which is suitable for complex underwater detection tasks.

[0141] The embodiment of the present invention provides a multi-beam sonar target detection and imaging method and system, which integrates multiple processing steps such as noise suppression, signal enhancement and target feature extraction through a full-process optimization algorithm and a joint processing strategy, which not only improves the detection accuracy and the reliability of target recognition, but also can adapt to complex underwater environments. At the same time, it can significantly improve the accuracy and efficiency of multi-beam detection. The system has broad application prospects in the fields of underwater environment monitoring, resource exploration and so on.

[0142] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0143] The above describes the specific embodiments of the present invention. It should be understood that the present invention 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 essence of the present invention. In the absence of 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: collect raw data through multi-beam and preprocess them, pass the acquired preprocessed image data to the noise suppression step, and further optimize the raw data sampling rate and filtering strategy based on the feedback of the noise suppression step; Noise suppression step: receiving the pre-processed image data, suppressing background noise and sidelobe interference in the pre-processed image data, and obtaining a water body image; Target extraction step: obtain water body images, extract the significant feature scale of the target, and improve the saliency of the target features in the water body images to complete the extraction of target information; Result analysis step: 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.

2. The multi-beam sonar target detection and imaging method according to claim 1, characterized in that: The data preprocessing step includes: Step 1.1: Obtain raw data from the multi-beam detection equipment, perform data format analysis and information extraction, and extract Doppler velocity data, inertial navigation data, and sonar image data; Step 1.2: Integrate the Doppler velocity data to obtain the velocity and displacement information of the target; Step 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality; Step 1.4: The multi-beam data processed in the above steps is output as pre-processed image information for subsequent target recognition and analysis.

3. The multi-beam sonar target detection and imaging method according to claim 1, characterized in that: The noise suppression step comprises: Step 2.1: Obtain preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection law; Step 2.2: Perform sector gap intensity compensation to correct data overlap or blank areas caused by detection angle overlap or insufficient signal strength; Step 2.3: Smoothing the noise in the preprocessed image data to reduce the noise interference in the preprocessed image data; Step 2.4: Perform angle sequence adaptive threshold calibration, dynamically adjust the threshold according to actual data, and optimize detection accuracy; Step 2.5: Apply multi-level threshold filtering to extract valid signals and suppress interference; Step 2.6: Enter the multi-ping accumulation bottom background estimation stage, estimate the bottom background information by accumulating multiple ping echo signals, and perform background suppression to eliminate the impact of bottom stray signals on image quality; Step 2.7: After noise suppression processing, water body image information is obtained to provide input for subsequent target analysis and detection.

4. The multi-beam sonar target detection and imaging method according to claim 1, characterized in that: The target extraction step comprises: Step 3.1: Input the water body image, estimate the noise scale of the water body image, analyze the distribution characteristics and scale information of the noise in the image, and estimate the maximum characteristic scale at the same time, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing; Step 3.2: Perform multi-scale local contrast enhancement on the water body image to enhance the local details and contrast, thereby improving the saliency of the target features in the image and obtaining an enhanced image; Step 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out; Step 3.4: Based on the extracted classification clusters, target feature analysis is performed to complete the extraction of target information.

5. The multi-beam sonar target detection and imaging method according to claim 1, characterized in that: The result analysis step comprises: Step 4.1: Perform intensity analysis, position 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 into a new format; Step 4.3: By matching the features in the early warning information database, a comprehensive multi-dimensional data analysis is performed to identify potential threats and abnormal situations and obtain analysis results; Step 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.

6. A multi-beam sonar target detection and imaging system, characterized in that: include: Data preprocessing module: collects raw data through multi-beam and preprocesses it, passes 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: receiving the pre-processed image data, suppressing background noise and sidelobe interference in the pre-processed image data, and obtaining a water body image; Target extraction module: obtains water body images, extracts the significant feature scale of the target, and improves the saliency of the target features in the water body image to complete the extraction of target information; Result analysis module: further analyzes the extracted target information, and transmits the analysis results to the host computer through the data interface for data feedback and correction, or displays and stores them within the system.

7. The multi-beam sonar target detection and imaging system according to claim 6, characterized in that: The preprocessing in the data preprocessing module includes: Module 1.1: Obtain raw data from multi-beam detection equipment, perform data format analysis and information extraction, and extract Doppler velocity data, inertial navigation data, and sonar image data; Module 1.2: Integrate the Doppler velocity data to obtain the target's velocity and displacement information; Module 1.3: Parse the inertial navigation data to obtain the target's attitude information and time-synchronize it with the velocity and displacement data; perform coordinate transformation on the sonar image data and then perform range-constrained filtering to remove noise and optimize image quality; Module 1.4: The multi-beam data processed by the above modules is output as pre-processed image information for subsequent target recognition and analysis.

8. The multi-beam sonar target detection and imaging system according to claim 6, characterized in that: The noise suppression module comprises: Module 2.1: Obtain preprocessed image data and perform outlier filtering on the angle sequence to remove abnormal data that does not conform to the detection rules; 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: Smoothing the noise in the preprocessed image data to reduce the noise interference in the preprocessed image data; Module 2.4: Perform angle sequence adaptive threshold calibration, dynamically adjust the threshold according to actual data, and optimize detection accuracy; Module 2.5: Apply multi-level threshold filtering to extract valid signals and suppress interference; Module 2.6: Enter the multi-ping accumulation underwater background estimation stage, estimate the underwater background information through the accumulation of multiple ping echo signals, and perform background suppression to eliminate the impact of underwater stray signals on image quality; Module 2.7: After noise suppression processing, water body image information is obtained to provide input for subsequent target analysis and detection.

9. The multi-beam sonar target detection and imaging system according to claim 6, characterized in that: The target extraction module comprises: Module 3.1: Input water body images, estimate the noise scale of water body images, analyze the distribution characteristics and scale information of noise in the images, estimate the maximum characteristic scale, extract the significant characteristic scale of the target, and provide basic parameters for subsequent processing; Module 3.2: Perform multi-scale local contrast enhancement on water body images to enhance the local details and contrast, improve the saliency of target features in the image, and obtain an enhanced image; Module 3.3: Perform fuzzy clustering analysis on the enhanced image, identify potential target areas by classifying pixel features, and extract stable classification clusters, from which target feature clusters with consistency and significance are screened out; Module 3.4: Based on the extracted classification clusters, perform target feature analysis to complete the extraction of target information.

10. The multi-beam sonar target detection and imaging system according to claim 6, characterized in that: The result analysis module comprises: Module 4.1: Perform strength analysis, position estimation and size calculation on target information to obtain basic characteristics of target information; Module 4.2: Integrate and convert the basic features of target information into different formats; Module 4.3: By matching the features in the early warning information database, conduct a comprehensive analysis of multi-dimensional data, identify potential threats and abnormal situations, and 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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