A high-resolution imaging method and system for a hydrophone array

Abstract

The present invention provides a high-resolution imaging method and system for a hydrophone array, which are applied to the field of imaging data processing. By drawing a sound field distribution diagram and analyzing imaging quality indicators, the present invention can monitor and adjust the array layout and signal acquisition strategy in real time to ensure that a high imaging quality is maintained under different environmental conditions. At the same time, by combining acoustic signals of different frequency bands and weighted fusion of frequency band characteristics, the imaging clarity can be improved under different depths and environmental conditions. By activating a phased array to control the phase relationship between each hydrophone and adjusting the beam direction in real time, the signal can be focused in a specific area, thereby improving the effectiveness of target detection and imaging and avoiding imaging blind spots.

Description

Technical Field

[0001] The present invention relates to the field of imaging data processing, and in particular to a high-resolution imaging method and system for a hydrophone array. Background Art

[0002] A hydrophone (or underwater microphone) is a sensor used to receive underwater sound wave signals and is widely used in the fields of ocean detection, acoustic positioning, underwater communication, submarine detection, etc. With the increasing demand for exploration and development of marine resources, underwater acoustic detection technology has put forward higher requirements in terms of accuracy and range.

[0003] Since the design and layout of the hydrophone array will greatly affect its imaging capabilities, different underwater environments (such as deep sea, shallow water, and complex terrain) require different array layouts and optimization solutions. Traditional arrays may have problems such as large spacing and unreasonable layout, resulting in reduced spatial resolution or imaging blind spots, and are unable to flexibly adapt to changing underwater environments. Summary of the invention

[0004] The present invention aims to solve the problem that the traditional hydrophone array has too large spacing or unreasonable layout, resulting in reduced spatial resolution or the existence of imaging blind areas, and provides a high-resolution imaging method and system for a hydrophone array.

[0005] The present invention adopts the following technical means to solve the technical problem:

[0006] The present invention provides a high-resolution imaging method of a hydrophone array, comprising:

[0007] Based on the pre-collected imaging data, identifying a preset target signal from the imaging data;

[0008] Determining whether the target signal is in signal noise;

[0009] If so, the preset layout information of the hydrophone array is applied to collect acoustic data in the water environment, a sound field distribution map of the water environment is drawn according to the acoustic data, the imaging quality of the target signal is obtained through the sound field distribution map, the imaging quality is substituted into the predefined imaging quality index, and the content to be optimized of the target signal is generated, wherein the layout information specifically includes the hydrophone spacing, arrangement mode and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area;

[0010] Determining whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization;

[0011] If not, activate the preset phased array to control the phase relationship between each hydrophone, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophones, and based on the frequency band characteristics of the acoustic signal, perform weighted fusion on the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

[0012] Furthermore, before the step of applying the preset layout information of the hydrophone array to collect acoustic data in the water environment, the method further includes:

[0013] Based on the detection waveform type preset by the hydrophone, a target area is selected from the water environment, and the transmit beam shape of the hydrophone array is switched according to the target area, and the signal energy is focused to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal;

[0014] determining whether the hydrophone can receive a reflected signal in the target area;

[0015] If not, an arrangement of the hydrophone array is generated, an automatic gain control parameter of the hydrophone is detected according to the arrangement, the ambient noise in the water environment is identified, and the signal gain of the hydrophone is dynamically adjusted based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array and a mixed array.

[0016] Furthermore, the step of drawing a sound field distribution map of the water environment according to the acoustic data and obtaining the imaging quality of the target signal through the sound field distribution map includes:

[0017] Calculating the relative distance between the hydrophone and the target signal based on preset phase information, extracting the scattering intensity of the target signal from the sound field distribution diagram, and acquiring the signal characteristics of the target signal;

[0018] Determining whether the signal characteristic exceeds a preset threshold;

[0019] If so, a preset time window sliding detection is used to detect the continuity of the target signal, and the moving trajectory of the target signal is tracked in real time in the sound field distribution diagram according to the continuity, and the motion process of the target signal is generated according to the moving trajectory, and the imaging quality fluctuation diagram of the target signal is drawn through the motion process.

[0020] Furthermore, the step of combining the phased array with different frequency bands to generate acoustic signals of different frequencies through the hydrophone includes:

[0021] Based on the preset signal sampling channel of the hydrophone array, activating the preset multi-channel synchronous sampler to collect signals of different frequency bands through the signal sampling channel with a unified sampling frequency to obtain acoustic signals of different frequencies;

[0022] Determining whether a preset time delay feature is detected in the acoustic signal;

[0023] If so, a preset cross-correlation method is used to calculate the similarity of the acoustic signal in different frequency bands, identify the time difference of the acoustic signal based on the similarity, and correct the acoustic signal through a preset time shift operation to align the acoustic signal in different frequency bands.

[0024] Furthermore, after the step of activating the preset phased array to control the phase relationship between the hydrophones and adjusting the beam direction in real time according to the phase relationship, the method further includes:

[0025] Based on the received signal of the hydrophone, detecting the phase difference of the received signal, performing spectrum phase analysis on the received signal according to the phase difference, and acquiring phase difference data of the hydrophone;

[0026] Determining whether the phase difference data matches a preset phase deviation value;

[0027] If not, then based on the phase compensation type of the phase difference data, a phase compensation formula is applied to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on the phase adjustment amount, wherein the phase compensation type specifically includes linear phase compensation and nonlinear phase compensation.

[0028] Furthermore, the step of determining whether the target signal is in signal noise further includes:

[0029] Based on the target echo signal detected by the preset receiver through the water environment, the target echo signal at the same target position is collected at least twice, and the target echo signal is coherently accumulated to generate a signal-to-noise ratio of the target echo signal;

[0030] Determining whether the signal-to-noise ratio reaches a preset decibel value;

[0031] If yes, a beam center direction is drawn according to the estimated position and movement direction of the target signal, and the phase of the signal received by the hydrophone is dynamically adjusted according to the beam center direction so that the signal phase is coherently superimposed along the beam center direction.

[0032] Furthermore, the step of identifying a preset target signal from the imaging data based on the pre-collected imaging data further includes:

[0033] Applying a preset wavelet transform to perform wavelet decomposition on the imaging data, decomposing the target signal into component results of different frequencies and time scales, and extracting each scale coefficient from the component results, wherein each scale coefficient specifically includes energy, mean value and standard deviation;

[0034] Determining whether the scale coefficients can be combined into a preset overall feature vector;

[0035] If possible, the overall feature vector is classified, the target signal is identified by classification, the extracted features of the overall feature vector are reconstructed through a preset inverse wavelet transform, and the reflection information of the extracted features on the target signal is verified.

[0036] The present invention also provides a high-resolution imaging system of a hydrophone array, comprising:

[0037] An identification module, configured to identify a preset target signal from the imaging data based on the pre-collected imaging data;

[0038] A judging module, used for judging whether the target signal is in signal noise;

[0039] an execution module, for, if yes, applying the preset layout information of the hydrophone array, collecting acoustic data in the water environment, drawing a sound field distribution map of the water environment according to the acoustic data, obtaining the imaging quality of the target signal through the sound field distribution map, substituting the imaging quality into a predefined imaging quality index, and generating content to be optimized for the target signal, wherein the layout information specifically includes the hydrophone spacing, arrangement and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area;

[0040] A second judgment module is used to judge whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization;

[0041] The second execution module is used to activate the preset phased array to control the phase relationship between each hydrophone if it is not possible, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophone, and based on the frequency band characteristics of the acoustic signal, weightedly fuse the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

[0042] Furthermore, it also includes:

[0043] A focusing module, for selecting a target area from the water environment based on a detection waveform type preset by the hydrophone, switching a transmit beam shape of the hydrophone array according to the target area, and focusing signal energy to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal;

[0044] A third judgment module is used to judge whether the hydrophone can receive the reflected signal in the target area;

[0045] The third execution module is used to generate an arrangement of the hydrophone array if not, detect the automatic gain control parameter of the hydrophone according to the arrangement, identify the ambient noise in the water environment, and dynamically adjust the signal gain of the hydrophone based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array and a mixed array.

[0046] Furthermore, the execution module also includes:

[0047] an acquisition unit, configured to calculate the relative distance between the hydrophone and the target signal based on preset phase information, extract the scattering intensity of the target signal from the sound field distribution diagram, and acquire the signal characteristics of the target signal;

[0048] A judging unit, used to judge whether the signal characteristic exceeds a preset threshold;

[0049] An execution unit is used to, if yes, use a preset time window sliding to detect the continuity of the target signal, track the moving trajectory of the target signal in real time in the sound field distribution diagram according to the continuity, generate the motion process of the target signal according to the moving trajectory, and draw an imaging quality fluctuation diagram of the target signal through the motion process.

[0050] The present invention provides a high-resolution imaging method and system for a hydrophone array, which has the following beneficial effects:

[0051] The present invention can monitor and adjust the array layout and signal acquisition strategy in real time by drawing the sound field distribution map and analyzing the imaging quality index, so as to ensure that a high imaging quality is maintained under different environmental conditions. At the same time, by combining acoustic signals of different frequency bands and weighted fusion of frequency band characteristics, the imaging clarity can be improved under different depths and environmental conditions. Moreover, by activating the phased array to control the phase relationship between each hydrophone and adjusting the beam direction in real time, the signal can be focused in a specific area, thereby improving the effectiveness of target detection and imaging and avoiding imaging blind spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a process flow of an embodiment of a high-resolution imaging method of a hydrophone array of the present invention;

[0053] Figure 2 The structure block diagram of an embodiment of a high-resolution imaging system of a hydrophone array of the present invention. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Reference Figure 1 , is a high-resolution imaging method of a hydrophone array in an embodiment of the present invention, comprising:

[0057] S1: Based on pre-collected imaging data, identifying a preset target signal from the imaging data;

[0058] S2: Determine whether the target signal is in signal noise;

[0059] S3: If yes, then apply the preset layout information of the hydrophone array, collect acoustic data in the water environment, draw a sound field distribution map of the water environment according to the acoustic data, obtain the imaging quality of the target signal through the sound field distribution map, substitute the imaging quality into the predefined imaging quality index, and generate the content to be optimized of the target signal, wherein the layout information specifically includes the hydrophone spacing, arrangement and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area;

[0060] S4: determining whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization;

[0061] S5: If not, activate the preset phased array to control the phase relationship between each hydrophone, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophones, and based on the frequency band characteristics of the acoustic signal, perform weighted fusion on the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

[0062] In this embodiment, the system identifies a preset target signal from the imaging data based on the pre-collected imaging data, and then the system determines whether the target signal is in signal noise to execute the corresponding steps; for example, when the system determines that the target signal is not in signal noise, the system will consider that the target signal is clear and not significantly interfered by noise, and the signal-to-noise ratio is high, indicating that the signal can be reliably identified and processed, and the data quality is good. In the absence of noise, the system will extract the time-frequency characteristics, morphological characteristics and motion trajectory data of the target signal, further analyze the nature and state of the target, and continuously monitor the quality of the signal, especially in complex water environments, where noise may change with time or environmental conditions. Once the signal-to-noise ratio decreases or the noise interference increases, the system can adjust the acquisition plan in time, including increasing signal enhancement or adjusting the beam direction, because the system has a fast response mechanism. Moreover, since the signal is not interfered by noise, the complex steps of noise suppression can be skipped, and computing resources can be concentrated on optimizing target imaging, and using the current data to generate high-quality imaging results. For example, when the system determines that the target signal is in signal noise, the system will think that the target signal is blurred and significantly interfered by noise. The system will apply the pre-set layout information of the hydrophone array, which specifically includes the hydrophone spacing, arrangement and depth, to collect acoustic data in the water environment. The acoustic data specifically includes angular resolution, distance The system uses the layout information of the hydrophone array and the acoustic data to better control the propagation path of the sound waves and reduce the impact of noise, thereby improving the ability to capture the target signal and optimizing the spatial resolution of the imaging. At the same time, according to the sound field distribution map and the imaging quality index, the blind spots in the imaging process can be effectively identified, and the layout and parameters of the hydrophone array can be adjusted to reduce the scope of the blind spots, thereby enhancing the overall The system can fully perceive the water environment, and by substituting the imaging quality into the predefined imaging quality index, the system can generate the content to be optimized of the target signal, which enables the system to have adaptive adjustment capabilities, and can dynamically adjust the layout and signal processing parameters of the hydrophone array under different underwater environments and noise conditions to improve the overall detection and imaging capabilities of the system. By analyzing the signal imaging quality in the sound field distribution map, the system can accurately adjust the array layout according to the spacing, arrangement and depth information of the hydrophone array to ensure that the target signal can be effectively captured and imaged in various underwater environments; then the system determines whether the content to be optimized of the target signal can improve the imaging resolution of the hydrophone after the optimization is completed, so as to execute the corresponding steps;For example, when the system determines that the content to be optimized of the target signal can improve the imaging resolution of the hydrophone after optimization, the system will consider that the optimized hydrophone array layout is effective, and by locking these parameters, the system can maintain this good state in subsequent detection and imaging tasks, and continue to collect target signals using the optimized system configuration to ensure that high-resolution images and signals can be obtained under the new configuration, and continue to dynamically monitor environmental noise, temperature and flow rate factors. On the basis of continuous monitoring data, fine-tuning is performed to further optimize the imaging effect; for example, when the system determines that the content to be optimized of the target signal cannot improve the imaging resolution of the hydrophone after optimization, the system will consider that the optimized hydrophone array layout is invalid, and the system will activate the pre-set phased array to control the phase relationship between each hydrophone, adjust the beam direction in real time according to the phase relationship, and use combined detection of different frequency bands in combination with the phased array to generate acoustic signals of different frequencies through the hydrophone based on the frequency band characteristics of the acoustic signal. The frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics. , weighted fusion of frequency band features to obtain fusion imaging results; the system can dynamically adjust the beam direction by activating the phased array and adjusting the phase relationship of the hydrophone in real time, and focus detection on different target areas. This precise beam control can effectively improve the imaging resolution and make up for the deficiency of traditional array layout that cannot improve the resolution. At the same time, weighted fusion of low-frequency and high-frequency features can make full use of the advantages of different frequency signals. Low-frequency signals have better penetration ability and are suitable for long-distance and deep-water target detection, while high-frequency signals have higher resolution and are suitable for fine imaging. Through frequency band fusion, the system can achieve a better balance between imaging range and resolution, and obtain clearer and more complete imaging results. In addition, the combined detection of different frequency bands is particularly suitable for complex underwater environments. In noisy or multi-path propagation environments, low-frequency signals can help penetrate noise and obstacles, while high-frequency signals can provide fine imaging in a smaller range. By fusing the characteristics of different frequency bands, the system can adapt to changing underwater conditions and improve the imaging quality in complex environments. ;

[0063] It should be noted that based on the frequency band characteristics of the acoustic signal, the frequency band characteristics are weighted and fused to obtain a fused imaging result. The specific example is as follows:

[0064] Hypothetical scenario 1: shipwreck location and fine structure imaging,

[0065] Background: A shipwreck was discovered in the deep sea. Due to the severe underwater noise interference and the fact that the shipwreck is located at a relatively deep seabed, the detection distance is relatively long. It is necessary to combine long-distance macro positioning with close-range fine structure imaging to achieve comprehensive detection.

[0066] When the application step is low frequency detection,

[0067] Features: Low-frequency sound waves can penetrate greater underwater distances and noise, providing the general outline and location of the wreck;

[0068] Detection results: The low-frequency signal captured the overall outline of the sunken ship, but due to the low resolution, it was difficult to distinguish the specific hull structure;

[0069] When the application step is high frequency detection,

[0070] Features: High-frequency sound waves can provide high-precision images at close range, suitable for identifying details of shipwrecks, such as hull cracks, decks, anchor chains, etc.

[0071] Detection results: The high-frequency signal captured part of the sunken ship (such as the deck and cabin) and clearly showed the damage details, but due to the rapid attenuation of the high-frequency signal, it was impossible to fully detect the entire sunken ship;

[0072] Perform weighted fusion.

[0073] Fusion strategy: In order to balance long-distance detection and fine imaging, the system assigns 60% weight to low-frequency signals to ensure the overall outline of the shipwreck; and 40% weight to high-frequency signals to enhance the details of local structures;

[0074] Fusion results: The final image generated not only contains the general shape and location information of the shipwreck, but also presents the details of the deck, cabin and other structures, providing richer information to help further archaeological research;

[0075] Low-frequency sound waves provide macroscopic detection capabilities for shipwrecks, allowing the location of the shipwreck to be accurately located, while high-frequency signals show local details of the shipwreck, helping archaeologists identify the location of hull damage and providing a basis for repair or protection. Through weighted fusion, the system avoids the limitations of single-band imaging, providing complete shipwreck imaging and showing the fine features of the structure.

[0076] Hypothetical scenario 2: Shallow sea fish behavior monitoring,

[0077] The background is that researchers need to monitor the movement trajectory of a group of small fish in shallow waters and their interaction with the surrounding environment. The fish move quickly and are small in size, so high-resolution imaging is required to capture the fine movements and dynamics of the fish.

[0078] When the application step is low frequency detection,

[0079] Features: Low-frequency sound waves are suitable for capturing the overall movement trajectory and large-scale movement of fish schools;

[0080] Detection results: Low-frequency signals provide the overall distribution and approximate trajectory of the fish school, but cannot distinguish the individuals in the school and their movements;

[0081] When the application step is high frequency detection,

[0082] Features: High-frequency sound waves are suitable for detecting the fine movements of individual fish, such as fin swings, queue changes, etc.

[0083] Detection results: High-frequency signals can clearly capture the movements and behaviors of each fish in the school, but they can only cover a part of the school, making it difficult to obtain overall information;

[0084] Perform weighted fusion.

[0085] Fusion strategy: Due to the rapid movement and small size of fish schools, the system sets the weight of high-frequency signals to 70% and the weight of low-frequency signals to 30%. This can capture the individual behavior of fish schools and obtain the overall movement direction of fish schools;

[0086] Fusion results: Through fusion, researchers can simultaneously see the movement direction of the fish school as a whole (provided by low-frequency signals) and the dynamic and behavioral characteristics of the individuals in the school (provided by high-frequency signals);

[0087] The fusion results not only show the overall trajectory of the fish school, but also observe the dynamics of the individual in detail, helping to study the clustering behavior and interaction patterns of the fish school. Through weighted fusion of different frequency bands, researchers can monitor the changes of the fish school in real time and adapt to the characteristics of the rapid movement of the fish school. This fusion technology helps to identify the collective behavior patterns of the fish school, such as the process of the fish school dispersing or closing, and analyze their relationship with environmental factors (such as water flow or other fish);

[0088] Hypothetical scenario 3: Seabed topography survey,

[0089] The background is that it is necessary to accurately survey the complex terrain of the seabed, including ridges, gullies, craters and other structures. Due to the diverse seabed environment, large noise interference, and some areas are relatively deep, the system needs to combine low-frequency and high-frequency signals for all-round imaging;

[0090] When the application step is low frequency detection,

[0091] Features: Low-frequency signals can penetrate complex waters on the seabed and detect deeper terrain contours;

[0092] Detection results: Low-frequency signals capture the macroscopic topographic structure of the seabed, such as the height of ridges and the depth of gullies, but cannot be refined to smaller topographic details;

[0093] When the application step is high frequency detection,

[0094] Features: High-frequency signals can capture terrain details within a smaller range, such as the edge shape of a crater, the distribution of seabed rocks, etc.

[0095] Detection results: High-frequency signals provide detailed topographic images and can distinguish subtle structures on the seabed, but their detection range is small and cannot cover the entire area;

[0096] Perform weighted fusion.

[0097] Fusion strategy: In order to obtain the overall contour and local details of the seabed at the same time, the system sets the weight of low-frequency signals to 50% and the weight of high-frequency signals to 50%. This balanced weighted fusion ensures the integrity of the macro-topography while providing local fine survey results.

[0098] Fusion results: The fused topographic map not only shows the overall shape of the submarine ridges and gullies, but also can distinguish the edge of the crater and the distribution of rocks, forming a complete and detailed topographic map;

[0099] Both overall terrain and local details: By fusing low-frequency and high-frequency signals, the system provides the breadth of macro-terrain survey and the depth of local terrain refinement, ensuring the accuracy of terrain survey. At the same time, this fusion method is not only applicable to the survey of seabed terrain, but also to the survey of other underwater environments such as reservoirs, lakes and other complex environments. Low-frequency signals help to conduct effective detection at long distances and in complex environments, while high-frequency signals provide high-resolution detailed imaging in local areas, which comprehensively improves the survey accuracy.

[0100] In summary, through the weighted fusion of low-frequency and high-frequency signals, the system can combine the advantages of different frequency bands to meet the needs of different scenarios, which not only improves the overall coverage capability of target detection, but also enhances the capture of local fine structures. The beneficial effects in each application scenario demonstrate the flexibility and wide adaptability of this technology, and it has significant application value in underwater detection, environmental survey and biological behavior monitoring.

[0101] In this embodiment, before the step S3 of collecting acoustic data in the water environment by using the preset layout information of the hydrophone array, the following steps are also included:

[0102] S301: based on the detection waveform type preset by the hydrophone, selecting a target area from the water environment, switching the transmit beam shape of the hydrophone array according to the target area, and focusing signal energy to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal;

[0103] S302: Determine whether the hydrophone can receive a reflected signal in the target area;

[0104] S303: If not, then generate an arrangement of the hydrophone array, detect the automatic gain control parameters of the hydrophone according to the arrangement, identify the ambient noise in the water environment, and dynamically adjust the signal gain of the hydrophone based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array, and a mixed array.

[0105] In this embodiment, the system selects a target area from the water environment based on the detection waveform type pre-set by the hydrophone, which specifically includes a linear frequency modulation signal and a pulse signal, and switches the transmit beam shape of the hydrophone array according to the target area. The signal energy is focused on the target area through the transmit beam shape, and then the system determines whether the hydrophone can receive the reflected signal in the target area to execute the corresponding steps; for example, when the system determines that the hydrophone can receive the reflected signal in the target area, the system will consider that the reception of the signal proves that the transmit waveform and beam focusing strategy of the hydrophone array are effective, and the target area has been correctly detected. The system will further detect the target through sound waves of different frequencies, and the low-frequency signal is used to determine the overall outline of the target, and the high-frequency signal is used to capture the details of the target. At the same time, according to the quality of the received signal, the parameters of the transmit waveform are adjusted (such as adjusting the frequency range and duration of the linear frequency modulation or pulse signal), and the beam shape is optimized to improve the signal focusing effect and target resolution. If the target is dynamic, the system will start the automatic tracking algorithm, adjust the transmit beam direction according to the real-time signal, and continuously track the moving trajectory of the target; for example, when the system determines that the hydrophone cannot receive the reflected signal in the target area, At this time, the system will think that the target area cannot be correctly detected, and the system will generate an arrangement of the hydrophone array, which specifically includes non-uniform array, sparse array and hybrid array. According to different arrangement methods, the automatic gain control parameters of the hydrophone are detected, the environmental noise in the water environment is identified, and the signal gain of the hydrophone is dynamically adjusted based on the environmental noise; the system adopts non-uniform array, sparse array or hybrid array, and different arrangement methods can change the spacing and angle between the hydrophones, avoid the spatial resolution blind area caused by uniform arrangement, increase the detection coverage, and adjust the signal gain of the hydrophone according to the environmental noise, dynamically adapt to different underwater environmental noise levels, when the noise is large, the system can automatically enhance the signal gain to amplify the reflected signal, and when the noise is small, reduce the gain to prevent signal oversaturation. This adaptive control mechanism improves the detection accuracy of the system in complex environments, and by identifying the noise characteristics in the water environment, the system can adjust the parameters of the hydrophone for different noise types (such as ship noise, marine biological noise, natural water flow noise). This dynamic adjustment based on noise optimizes the detection performance, so that the system can adaptively adjust and maintain a high detection efficiency in various complex underwater conditions.

[0106] In this embodiment, the step S3 of drawing a sound field distribution map of the water environment according to the acoustic data and obtaining the imaging quality of the target signal through the sound field distribution map includes:

[0107] S31: calculating the relative distance between the hydrophone and the target signal based on preset phase information, extracting the scattering intensity of the target signal from the sound field distribution diagram, and acquiring the signal characteristics of the target signal;

[0108] S32: Determine whether the signal characteristic exceeds a preset threshold;

[0109] S33: If so, a preset time window is used to slide and detect the continuity of the target signal, and the moving trajectory of the target signal is tracked in real time in the sound field distribution diagram according to the continuity, and the motion process of the target signal is generated according to the moving trajectory, and the imaging quality fluctuation diagram of the target signal is drawn through the motion process.

[0110] In this embodiment, the system calculates the relative distance between the hydrophone and the target signal based on the pre-set phase information, extracts the scattering intensity of the target signal from the sound field distribution diagram, obtains the signal characteristics of the target signal, and then the system determines whether the signal characteristics exceed the pre-set threshold value to execute the corresponding steps; for example, when the system determines that the signal characteristics of the target signal do not exceed the pre-set threshold value, the system will consider that the current hydrophone array parameters or detection waveform settings fail to effectively capture the target signal, and the detection effect is not ideal. The system will use signal enhancement technology, such as coherent accumulation and automatic gain control, to amplify the strength of the target signal so that the signal characteristics can exceed the pre-set threshold value. When the preset threshold is exceeded, the beam shape or emission angle of the hydrophone array is adjusted to refocus the signal energy to the target area, increase the intensity of the signal echo, and switch to other frequency bands or adjust the detection waveform (such as linear frequency modulation signal or pulse signal) according to the characteristics of the target to optimize the detection effect and enhance the ability to capture the target. In addition, according to the environmental noise conditions in the target area, the signal gain of the hydrophone is dynamically adjusted to amplify the target signal strength without excessive interference from noise. For example, when the system determines that the signal characteristics of the target signal exceed the preset threshold, the system will consider that the current hydrophone array can effectively capture the target signal, and the system will automatically detect the target signal. The system will use a preset time window sliding to detect the continuity of the target signal, and based on the continuity, track the moving trajectory of the target signal in real time on the sound field distribution map, generate the movement process of the target signal based on the movement trajectory, and draw the imaging quality fluctuation map of the target signal through the movement process; by using the time window sliding to detect the continuity of the target signal, the system can dynamically monitor the changes of the target signal over a period of time to ensure the accuracy of the tracking results, which helps to maintain stable tracking of the target, especially when the target moves quickly or the signal strength fluctuates, to ensure that the signal will not be suddenly interrupted. At the same time, through the sound field distribution map, the system can draw in real time according to the continuity of the target signal. The moving trajectory of the target, the dynamically generated moving trajectory not only helps to track the target position in real time, but also can reveal the target's movement mode (such as straight line, curve, acceleration, etc.), and according to the movement process of the target signal, the system can draw an imaging quality fluctuation diagram to reflect the changes in imaging quality in real time. This visual chart can clearly show the changing trends of imaging quality such as signal strength, clarity and signal-to-noise ratio over time and target movement, and the imaging quality fluctuation diagram can be used to identify time periods or areas with poor imaging quality, helping the system to adjust detection parameters in time, such as the beam shape of the hydrophone array, frequency band switching and gain control, to improve the detection accuracy of the target area.

[0111] It should be noted that the system reflects the reflection of sound waves on the target surface by extracting the scattering intensity of the target signal. By analyzing the changes in the scattering intensity received at different positions, the system can more accurately determine the direction and depth of the target; the stronger the scattering intensity, the clearer the relative position between the target and the hydrophone, thereby improving the accuracy of target positioning. The scattering intensity can be used to estimate the size and shape of the target object. Different objects have different scattering behaviors for sound waves in water. Larger targets will produce stronger scattering signals, while small targets or objects with smooth surfaces will produce weaker scattering signals. In the sound field distribution map, the scattering intensity of the target directly affects the imaging quality. The greater the scattering intensity, the clearer the target area in the imaging will be and the higher the contrast. By extracting and analyzing the scattering intensity, the system can optimize the image resolution and clarity in subsequent imaging processing.

[0112] It should be supplemented that the relative distance between the hydrophone and the target signal is calculated based on the preset phase information. The specific example is as follows:

[0113] Assume that there is a hydrophone array consisting of three hydrophones (A, B, C), and they are arranged underwater at a certain interval. The sound wave signal emitted by the target object is received by the hydrophone array. The system calculates the relative distance between the target and each hydrophone based on the sound wave phase information received by each hydrophone;

[0114] Target position setting: Assume that the target is 150 meters away from hydrophone A, 152 meters away from hydrophone B, and 148 meters away from hydrophone C;

[0115] Receive phase difference:

[0116] The acoustic signal received by hydrophone A is used as a reference, and the initial phase of the signal is 0 degrees;

[0117] Since hydrophone B is slightly farther away from the target, the signal will be delayed to a certain extent, with a phase difference of +10 degrees;

[0118] Since hydrophone C is slightly closer to the target, the signal will arrive earlier, with a phase difference of -12 degrees;

[0119] Phase difference and distance calculation:

[0120] According to the formula (in, is the phase difference, is the distance difference, is the wavelength of the signal) The system can calculate the distance difference between each hydrophone and the target based on the phase difference, for example:

[0121] The phase difference between hydrophone A and B is +10 degrees. If the sound wave frequency used is 1kHz, the wavelength is If the distance is about 1.5 meters, the calculated distance difference is about 2 meters, which means that the target distance B is 2 meters farther than A;

[0122] The phase difference between hydrophones A and C is -12 degrees, and the distance difference is about -1.8 meters, which means that target C is about 1.8 meters closer than A;

[0123] In summary, the system calculates the relative distance between the hydrophone and the target signal through the preset phase information, and can infer the position of the target based on the phase difference. This method can provide extremely high accuracy and real-time performance in underwater detection, target tracking and imaging applications, and can adapt to various complex underwater environments.

[0124] The continuity of the target signal is detected by sliding a preset time window, and the moving trajectory of the target signal is tracked in real time in the sound field distribution diagram according to the continuity. The specific examples are as follows:

[0125] Suppose an underwater unmanned vehicle (AUV) is using a hydrophone array to detect and track a moving whale. In order to determine the continuous movement trajectory of the whale, the system will detect the continuity of the whale signal by sliding the time window and mark its movement trajectory in the sound field distribution map;

[0126] Step 1: Initial detection: The submersible detects the whale's acoustic signal for the first time and locates the whale's initial position through the hydrophone array. The system collects the whale's signal characteristics (including position, scattering intensity, etc.) within a time window (e.g. 1 second) and records them on the sound field distribution map.

[0127] Step 2: Time window sliding detection: After each time window (e.g. 1 second), the system will slide to the next time period and continue to detect whale signals. The system compares the changes in whale signals in the current time window with those in the previous time window. If continuous signals are detected and the position changes regularly, the signal is considered to be continuous, indicating that the whale is moving steadily in the water.

[0128] Step 3: Real-time tracking of movement trajectory: The system marks the current position of the whale in each time window on the sound field distribution map and connects each position point to form the movement trajectory of the whale. If the system detects stable signal changes in multiple consecutive time windows (for example, the whale moves at a speed of 2 meters per second toward the southeast), the system can predict the movement trajectory of the whale in the next few seconds;

[0129] Step 4: Detection of sudden changes: If a whale suddenly changes direction within a certain time window, the system will detect a significant change in the signal characteristics (for example, a jump in position or a fluctuation in signal strength). At this time, the system will mark this change as a possible motion change point and adjust the subsequent trajectory prediction;

[0130] Step 5: Processing of noise interference: If the system finds that the signal becomes discontinuous (for example, the signal becomes weaker or disappears) within a certain time window, it may be due to noise interference or the whale diving into deeper waters. The system will re-detect and confirm the signal status in the next time window. If the signal is restored, the tracking will continue; if the signal is not restored, it may be determined that the target is temporarily lost;

[0131] In summary, the system uses a preset time window to slide and detect the continuity of the target signal, which can effectively track the moving trajectory of the target signal in real time in the sound field distribution map. Through this method, the system can provide accurate target position change information and judge the target's motion state based on continuity, further improving the accuracy and efficiency of underwater detection and tracking.

[0132] In this embodiment, the step S5 of using the phased array to detect in different frequency bands and generate acoustic signals of different frequencies through the hydrophone includes:

[0133] S51: based on the preset signal sampling channel of the hydrophone array, activating the preset multi-channel synchronous sampler to collect signals of different frequency bands through the signal sampling channel with a unified sampling frequency to obtain acoustic signals of different frequencies;

[0134] S52: Determine whether the acoustic signal detects a preset time delay feature;

[0135] S53: If yes, a preset cross-correlation method is used to calculate the similarity of the acoustic signal in different frequency bands, the time difference of the acoustic signal is identified according to the similarity, and the acoustic signal is corrected by a preset time shift operation to align the acoustic signal in different frequency bands.

[0136] In this embodiment, the system activates the pre-set multi-channel synchronous samplers through the signal sampling channel based on the pre-set signal sampling channel of the hydrophone array, applies a unified sampling frequency to collect signals of different frequency bands, and obtains acoustic signals of different frequencies. Then the system determines whether these acoustic signals detect the pre-set time delay characteristics to execute the corresponding steps; for example, when the system determines that the acoustic signals of different frequencies do not detect the pre-set time delay characteristics, the system will believe that there may be errors in the propagation path of the signal, and the system will adjust the parameters of the signal sampling channel, such as increasing the sampling frequency and increasing the sampling time. Or improve the sampling resolution of the signal to ensure that the time delay feature is more obvious in the data, and enable noise suppression and filtering technology to remove environmental noise and extract clean acoustic signals to improve the detection capability of time delay features. Re-evaluate the propagation path of the sound wave according to the current water environment conditions (such as sound speed distribution, obstacle location, etc.). If the problem lies in the unreasonable array layout (such as array element spacing or arrangement), the system can optimize the delay detection effect by changing the array configuration, such as using sparse or mixed arrays; for example, when the system determines that acoustic signals of different frequencies detect a preset delay feature, At this time, the system will think that there is no error in the propagation path of the signal. The system will use the pre-set cross-correlation method to calculate the similarity of acoustic signals in different frequency bands, identify the time difference of acoustic signals based on the similarity, and correct the acoustic signals through the pre-set time shift operation to align the acoustic signals in different frequency bands. Since acoustic signals in different frequency bands have different characteristics, low-frequency signals often have stronger penetration, while high-frequency signals provide better spatial resolution. By correcting the time difference, the signals in different frequency bands are aligned in time sequence, which helps the system to better integrate these signal features, thereby improving the imaging accuracy and target recognition ability. At the same time, the similarity of signals in different frequency bands is calculated, and the arrival time difference of the signals is accurately aligned through time shift correction, which directly improves the accuracy of target positioning. Especially in complex underwater acoustic environments, the alignment of multi-band signals is crucial for accurately determining the position and motion trajectory of the target. After the acoustic signal is time-shifted, the system can suppress noise more effectively. The aligned signals have higher correlation in different frequency bands, which can make it easier for the system to identify the difference between target signals and noise, thereby improving the signal-to-noise ratio and reducing the interference of noise on signal detection and processing.

[0137] It should be noted that a preset cross-correlation method is used to calculate the similarity of the acoustic signal in different frequency bands, and the time difference of the acoustic signal is identified according to the similarity. The acoustic signal is corrected by a preset time shift operation so that the acoustic signal is aligned in different frequency bands. The specific examples are as follows:

[0138] Suppose a hydrophone array is used to detect a submarine in an area, and low-frequency signals (100 Hz) and high-frequency signals (1 kHz) are collected respectively. The high-frequency signal is delayed by 20 milliseconds compared to the low-frequency signal.

[0139] Specific steps:

[0140] 1. Signal acquisition,

[0141] Low-frequency signal: Assume that the sample data of the low-frequency signal obtained within 1 second is as follows:

[0142] x(t)=[0,0.31,0.58,0.81,0.95,...] (noise-processed values);

[0143] High-frequency signal: Sample data of the high-frequency signal (with a 20 millisecond delay) is as follows:

[0144] y(t)=[0,0.98,0.87,0.52,0.14,...] (corresponding to the value of t+0.02t+0.02t+0.02 seconds)

[0145] 2. Calculate cross correlation,

[0146] Select the time window: select 1 second as the time window and observe 1000 sample points (assuming the sampling frequency is 1000 Hz);

[0147] Step-by-step calculation: For each possible time shift (for example, from -0.1 seconds to 0.1 seconds, increasing by 0.01 seconds each time), calculate the product of the low-frequency signal x(t) and the high-frequency signal y(t+τ) and sum them to obtain the cross-correlation value;

[0148] Example calculation: Assume that when τ=0.02 seconds, the calculated cross-correlation value is the maximum value, which means that the high-frequency signal is delayed by 20 milliseconds relative to the low-frequency signal;

[0149] 3. Identify time differences,

[0150] Find the maximum cross-correlation value: Among the multiple τ values ​​calculated, record the maximum value of the cross-correlation and the corresponding time shift, assuming that the maximum value is at τ = 0.02 seconds;

[0151] 4. Time shift operation for signal correction,

[0152] Correct the high-frequency signal: time-shift the high-frequency signal y(t) to align it with the low-frequency signal x(t); for example, take the sample data of the high-frequency signal and modify it to: y'(t)=[0.87,0.52,0.14,...] (equivalent to shifting the signal to the left by 20 milliseconds);

[0153] 5. Verify the alignment results,

[0154] Visualize the alignment effect: Use the drawing tool to draw the waveform of the low-frequency signal and the corrected high-frequency signal, and check whether their peaks coincide by observing the intersection of the two curves at the same time point;

[0155] 6. Fusion signal,

[0156] Fusion processing: The corrected high-frequency signal and low-frequency signal can be fused; for example, simply add the two signals to get the composite signal, and the sample of the composite signal = x(t) + y'(t);

[0157] In summary, the system uses the cross-correlation method to effectively identify the time difference between the low-frequency signal and the high-frequency signal, and adjusts the high-frequency signal to be synchronized with the low-frequency signal through time-shift operation. As a result, the accuracy of the signal is improved, providing a more reliable basis for subsequent data analysis and target detection.

[0158] In this embodiment, after activating the preset phased array to control the phase relationship between the hydrophones and adjusting the beam direction in real time according to the phase relationship in step S5, the method further includes:

[0159] S501: Based on the received signal of the hydrophone, detecting the phase difference of the received signal, performing spectrum phase analysis on the received signal according to the phase difference, and acquiring phase difference data of the hydrophone;

[0160] S502: Determine whether the phase difference data matches a preset phase deviation value;

[0161] S503: If not, then according to the phase compensation type of the phase difference data, apply the phase compensation formula to calculate the phase adjustment amount of the hydrophone, and dynamically update the phase compensation amount of the hydrophone in the water environment based on the phase adjustment amount, wherein the phase compensation type specifically includes linear phase compensation and nonlinear phase compensation.

[0162] In this embodiment, the system detects the phase difference of the received signal based on the received signal of the hydrophone, performs spectral phase analysis on the received signal according to different phase differences, obtains the phase difference data of the hydrophone, and then the system determines whether these phase difference data match the preset phase deviation value to execute the corresponding steps; for example, when the system determines that the phase difference data of the hydrophone can match the preset phase deviation value, the system will consider that the signal has not been significantly interfered with during the propagation process, and the propagation path is relatively stable, the system will record and analyze the current signal quality, save the matched phase difference data and related signal characteristics (such as amplitude, frequency, etc.) to the database for subsequent analysis and reference, and draw a sound field distribution map of the water environment based on the phase difference and other signal characteristics to help visualize the acoustic characteristics and target position, and adjust the detection parameters in real time according to the current environment and target status, such as the layout or sampling frequency of the hydrophone array, to improve the detection efficiency; for example, when the system determines that the phase difference data of the hydrophone cannot match the preset phase deviation value, the system will consider that the signal is interfered with during propagation, and the system will Phase compensation type of phase difference data, which specifically includes linear phase compensation and nonlinear phase compensation. The phase compensation formula is used to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on different phase adjustment amounts. Through phase compensation, the system can correct the phase deviation caused by environmental interference (such as water flow, clutter, temperature gradient, etc.). This correction can significantly restore the original quality of the signal, improve the signal-to-noise ratio, and make the received signal clearer and more stable, providing a good foundation for subsequent signal processing. At the same time, through real-time updated phase compensation, signal processing algorithms (such as beamforming, filtering, and feature extraction, etc.) can run on the basis of more accurate signals, reduce processing complexity, improve processing efficiency, and enhance the accuracy of processing results, making subsequent analysis more reliable. Based on real-time feedback of the phase adjustment amount, the system can intelligently adjust the detection strategy, such as the system can change the configuration of the hydrophone array, the signal acquisition frequency or the sampling method, thereby improving the detection efficiency under specific environmental conditions. This dynamic adaptability enables the system to respond flexibly when facing different water environments.

[0163] It should be noted that, according to the phase compensation type of the phase difference data, the phase compensation formula is applied to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on the phase adjustment amount. The specific examples are as follows:

[0164] Phase compensation is usually divided into two types,

[0165] Linear phase compensation: Applicable to situations where the signal is uniformly disturbed during propagation, such as uniform water flow velocity, stable temperature gradient, etc. The formula for linear phase compensation is usually:

[0166]

[0167] in, is the phase adjustment factor, is the change of propagation distance;

[0168] Nonlinear phase compensation: Applicable to situations where the signal is subject to uneven interference during propagation, such as drastic changes in water flow and multipath propagation;

[0169] Then calculate the phase adjustment amount. Assuming that there is a phase difference in the signal received by the system, the required phase adjustment amount can be calculated using the phase compensation formula. Take linear phase compensation as an example:

[0170] Detect phase difference: The system analyzes the received signal and finds that the phase difference is ;

[0171] Calculating the desired phase: Setting a target phase ;

[0172] Calculate the phase adjustment: ;

[0173] The phase compensation amount is then dynamically updated. Once the phase adjustment amount is calculated, the system will dynamically update the phase compensation setting of the hydrophone based on this amount. The specific steps are as follows:

[0174] The calculated The control system applied to the hydrophone adjusts the phase of its transmission and reception. After the update, the system continues to monitor the signal received by the hydrophone, and the phase difference data will be analyzed again to confirm the effectiveness of the compensation. If the phase difference still exists, it will be adjusted according to the new phase difference. This process can be repeated to ensure that the system always maintains the best state in a dynamic environment.

[0175] Assume that the phase of the signal received by a hydrophone at a specific location is , and the target phase is set to ; Then calculate the phase adjustment amount:

[0176]

[0177] The system increases the transmit and receive phases of the hydrophone by 30° and re-detects the phase of the received signal. If the new phase is still 40° after adjustment, the system will calculate again:

[0178]

[0179] Finally, the phase is updated and the signal monitoring is performed again;

[0180] In summary, the system ensures that the hydrophone can adapt to changes in complex water environments and maintain efficient detection capabilities through the selection and calculation of phase compensation types and the dynamic update of phase adjustment amounts. This flexible adjustment mechanism is very important for dealing with various dynamic interferences and can effectively improve signal quality and target recognition capabilities.

[0181] In this embodiment, the step S2 of determining whether the target signal is in signal noise further includes:

[0182] S21: based on the target echo signal detected by the preset receiver through the water environment, the target echo signal of the same target position is collected at least twice, and the target echo signal is coherently accumulated to generate a signal-to-noise ratio of the target echo signal;

[0183] S22: Determine whether the signal-to-noise ratio reaches a preset decibel value;

[0184] S23: If yes, draw a beam center direction according to the estimated position and movement direction of the target signal, and dynamically adjust the signal phase received by the hydrophone according to the beam center direction so that the signal phase is coherently superimposed along the beam center direction.

[0185] In this embodiment, the system collects the target echo signals at the same target position at least twice based on the target echo signals monitored by the water environment by the pre-set receiver, and performs coherent accumulation on these target echo signals to generate the signal-to-noise ratio of the target echo signals, and then the system determines whether these signal-to-noise ratios reach the preset decibel value to execute the corresponding steps; for example, when the system determines that the signal-to-noise ratio of the target echo signal cannot reach the preset decibel value, the system will consider that the target echo signal is seriously interfered by noise, which may cause inaccurate or completely unrecognizable target detection. The system will reconfigure the hydrophone array according to the environmental noise situation, use an array configuration that is more suitable for the current water environment, such as a sparse array or a hybrid array, and dynamically adjust the filtering parameters according to the real-time signal characteristics to enhance the target signal, extract the effective target echo signal, and analyze the possible interference sources to confirm the cause of the low signal-to-noise ratio. If the current detection waveform is not suitable, different signal types can be tried, such as using a linear frequency modulation signal to better handle long-distance detection and improve distance resolution, such as using a pulse signal at a target distance. Provide a higher signal-to-noise ratio when the distance is closer; for example, when the system determines that the signal-to-noise ratio of the target echo signal can reach the preset decibel value, the system will consider that the target detection can be accurately identified, and the system will draw the beam center direction according to the pre-estimated position and movement direction of the target signal, and dynamically adjust the signal phase received by the hydrophone according to the beam center direction, so that the signal phase is coherently superimposed along the beam center direction; through coherent superposition, the system can enhance the strength of the target signal, thereby improving the overall signal-to-noise ratio, making the target echo signal more obvious, easy to identify and analyze, and dynamically adjust the signal phase received by the hydrophone to make it coherently superimposed along the beam center direction, which helps to concentrate signal energy, significantly improve the accuracy of target detection, and reduce position errors. The beam center direction is drawn based on the pre-estimated position and movement direction of the target signal to ensure that the system can adjust in real time, thereby quickly adapting to the movement of the target, which is very important for dynamic monitoring. By accurately controlling the phase of the hydrophone, the system can use its resources more effectively and focus signal processing on the most relevant direction, thereby improving detection efficiency and reducing unnecessary interference.

[0186] It should be noted that the beam center direction is drawn according to the estimated position and movement direction of the target signal, and the phase of the signal received by the hydrophone is dynamically adjusted according to the beam center direction, so that the signal phase is coherently superimposed along the beam center direction. The specific example is as follows:

[0187] Suppose a hydrophone array is monitoring a submarine that is moving at a certain speed underwater. Let's analyze step by step how to dynamically adjust the beam center direction based on the estimated position and direction of movement of the target signal;

[0188] Scene setting,

[0189] Target type: Submarine;

[0190] Initial position: Assume that the system predicts that the submarine is at (100 meters, 200 meters);

[0191] Direction of movement: The target is moving towards the north (0°) at a speed of 1 m / s;

[0192] Hydrophone array: consists of multiple hydrophones configured in a rectangular array to receive and transmit signals;

[0193] The specific steps are:

[0194] 1. Target position monitoring: The system determines that the target is located at (100, 200) at a certain moment based on the previous echo signal and target position estimation. Assume that after a certain period of time (for example, 10 seconds), the target has moved to (100, 210);

[0195] 2. Determine the direction of movement. The system uses Kalman filtering to analyze past signal data and determines that the submarine is moving in the north (0°). Based on the speed, the system can predict that the submarine will move to (100, 211) at the next time point.

[0196] 3. Draw the beam center direction. Determine the beam center direction by calculating the straight line from the hydrophone array to the current target position. For example, the direction of the hydrophone points to the coordinates of the submarine (100, 210).

[0197] 4. Dynamically adjust the signal phase. Assuming that the phase of the signal received by the hydrophone at this moment is 15°, in order to make the signal coherently superimposed along the beam center direction (0°), calculate the phase adjustment amount: , which means that the phase of the hydrophone's transmitting signal needs to be adjusted by 15°;

[0198] 5. Implement coherent superposition. The adjusted signal is sent out, and the received echo signal is superimposed after phase adjustment. If the signal phases of other hydrophones are 20° and 10° respectively, they also need to be adjusted accordingly. All hydrophones are phase-adjusted according to their differences from the beam center direction. After adjustment, coherent superposition will increase signal strength and reduce background noise.

[0199] 6. Real-time monitoring and feedback. The system continuously monitors the movement of the submarine and regularly updates its position and phase adjustment. Assuming that an update is performed every 1 second, the phase of the transmitted and received signals is adjusted in real time. At the new position (100, 211) and the new phase deviation, the signal is processed and superimposed again.

[0200] In summary, through this dynamic adjustment process, the submarine's echo signal is more obvious in the noise background, ensuring that the submarine is accurately tracked. That is, the system can dynamically adjust the beam direction according to the movement of the target to maintain the focus of the signal, and through coherent superposition, the final signal-to-noise ratio is significantly improved, making subsequent analysis more accurate.

[0201] In this embodiment, the step S1 of identifying a preset target signal from the imaging data based on the pre-collected imaging data further includes:

[0202] S11: applying a preset wavelet transform to perform wavelet decomposition on the imaging data, decomposing the target signal into component results of different frequencies and time scales, and extracting each scale coefficient from the component results, wherein each scale coefficient specifically includes energy, mean value and standard deviation;

[0203] S12: Determine whether the scale coefficients can be combined into a preset overall feature vector;

[0204] S13: If yes, classify the overall feature vector, identify the target signal through classification, reconstruct the extracted features of the overall feature vector through a preset inverse wavelet transform, and verify the reflection information of the extracted features on the target signal.

[0205] In this embodiment, the system applies a pre-set wavelet transform to perform wavelet decomposition on the imaging data, decomposes the target signal into component results of different frequencies and time scales, extracts each scale coefficient from the component results, and each scale coefficient specifically includes energy, mean and standard deviation, and then the system determines whether these scale coefficients can be combined into a pre-set overall feature vector to execute the corresponding steps; for example, when the system determines that the scale coefficients cannot be combined into a pre-set overall feature vector, the system will consider that it means that the characteristics of the target signal are insufficient for effective identification or classification, and the system will adjust the mother wavelet type and decomposition layer number of the wavelet transform to better adapt to the frequency characteristics of the target signal, select a suitable time scale to capture the transient characteristics of the signal, and apply a filter (such as a low-pass or high-pass filter) to remove noise and improve signal quality, and re-collect data to ensure the integrity and accuracy of the signal, monitor and correct potential problems in the data collection process; for example, when the system determines that the scale coefficients can be combined into a pre-set overall feature vector, the system will consider that the target signal is not sufficiently characterized. The characteristics of the signal are sufficient to be recognized and classified by the hydrophone. The system will classify the overall feature vector, classify and identify the target signal, and reconstruct the extracted features of the overall feature vector through a pre-set inverse wavelet transform to verify the information reflected by the extracted features on the target signal. By classifying the overall feature vector, the system can more accurately identify the target signal, reduce the misjudgment rate, and improve the reliability of recognition. Because the overall feature vector integrates multi-scale information, it can more comprehensively reflect the characteristics of the target signal, allowing the classifier to better capture the key features of the target. At the same time, by reconstructing the extracted features through the inverse wavelet transform, it can be directly verified whether these features truly and effectively reflect the essence of the target signal, thereby enhancing the credibility of the system. The reconstruction process also provides feedback for further adjustment and optimization of feature extraction and classification methods, which helps to continuously improve system performance. Moreover, through feature extraction and classification, it can reduce the direct processing of large amounts of raw data, reduce computational complexity, and convert complex target signals into relatively concise feature vectors, making subsequent processing, storage, and analysis more efficient.

[0206] It should be noted that the overall feature vector is classified, the target signal is classified and identified, the extracted features of the overall feature vector are reconstructed through a preset inverse wavelet transform, and the reflection information of the extracted features on the target signal is verified. The specific examples are as follows:

[0207] Assume that the system analyzes the collected echo signal through wavelet transform, and the obtained scale coefficients are:

[0208] Energy of frequency 1: 0.2;

[0209] Energy of frequency 2: 0.5;

[0210] Energy of frequency 3: 0.3;

[0211] Combine these energy values ​​into an overall feature vector: [0.2, 0.5, 0.3][0.2, 0.5, 0.3][0.2, 0.5, 0.3];

[0212] The system selects support vector machine as the classification algorithm and prepares a labeled data set, which includes feature vectors and corresponding categories (such as "fish", "obstacle", "wreck", etc.) of multiple known target signals. The labeled data set is used for model training. The system learns through feature vectors and corresponding categories to establish a mapping relationship between target signals and features. Assume that after the model is learned, the following decision boundary is obtained:

[0213] The feature vector [0.2, 0.5, 0.3][0.2, 0.5, 0.3][0.2, 0.5, 0.3] is judged as "fish";

[0214] Input the current overall feature vector [0.2, 0.5, 0.3][0.2, 0.5, 0.3][0.2, 0.5, 0.3] into the trained SVM model and obtain the classification result as "fish";

[0215] Feature reconstruction, using inverse wavelet transform to extract the feature vector:

[0216] [0.2,0.5,0.3][0.2,0.5,0.3][0.2,0.5,0.3], convert back to the original signal form, assuming that the inverse transformed signal is: [0.15,0.45,0.35,0.25][0.15,0.45,0.35,0.25][0.15,0.45,0.35,0.25];

[0217] Then verify the extraction effect. For the target identified as "fish", the system needs to confirm whether the reconstructed signal can effectively reflect the real echo signal of the target;

[0218] During the verification process, it is assumed that the original acquired echo signal is:

[0219] [0.1,0.4,0.3,0.2][0.1,0.4,0.3,0.2][0.1,0.4,0.3,0.2];

[0220] Then calculate the mean square error (MSE) between the reconstructed signal and the original signal:

[0221]

[0222] x=[0.15,0.45,0.35,0.25];

[0223] y=[0.1,0.4,0.3,0.2];

[0224]

[0225] If the calculated MSE is 0.00375, and the threshold set by the system is 0.01, it is obvious that the MSE of the reconstructed signal is lower than the threshold, indicating that the extracted features are successful and can effectively reflect the information of the target signal. On the contrary, if the MSE exceeds the threshold, the system may re-extract features or adjust the classifier;

[0226] In summary, the system can effectively extract target signal features through wavelet transform and verify the extraction effect through classification and reconstruction, which not only improves the recognition accuracy of target signals, but also provides a reliable basis for subsequent dynamic monitoring and intelligent decision-making. This process ensures the accurate capture and analysis of target signals in complex water environments.

[0227] Reference Figure 2 , is a high-resolution imaging system of a hydrophone array in an embodiment of the present invention, comprising:

[0228] The recognition module 10 is used to recognize a preset target signal from the imaging data based on the pre-collected imaging data;

[0229] A judging module 20, configured to judge whether the target signal is in signal noise;

[0230] An execution module 30 is used for, if yes, applying the preset layout information of the hydrophone array, collecting acoustic data in the water environment, drawing a sound field distribution map of the water environment according to the acoustic data, obtaining the imaging quality of the target signal through the sound field distribution map, substituting the imaging quality into a predefined imaging quality index, and generating the content to be optimized of the target signal, wherein the layout information specifically includes the hydrophone spacing, arrangement mode and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area;

[0231] A second judgment module 40 is used to judge whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization;

[0232] The second execution module 50 is used to activate the preset phased array to control the phase relationship between each hydrophone if it is not possible, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophone, and based on the frequency band characteristics of the acoustic signal, weightedly fuse the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

[0233] In this embodiment, the recognition module 10 recognizes a preset target signal from the imaging data based on the imaging data collected in advance, and then the judgment module 20 judges whether the target signal is in signal noise to execute the corresponding steps; for example, when the system determines that the target signal is not in signal noise, the system will consider that the target signal is clear, not significantly interfered by noise, and has a high signal-to-noise ratio, indicating that the signal can be reliably identified and processed, and the data quality is good. In the absence of noise, the system will extract the time-frequency characteristics, morphological characteristics and motion trajectory data of the target signal, further analyze the nature and state of the target, and continuously monitor the quality of the signal, especially in complex water environments where noise may decrease over time or environment. The system is generated by changes in environmental conditions. Once the signal-to-noise ratio decreases or the noise interference increases, the system can adjust the acquisition plan in time, including increasing signal enhancement or adjusting the beam direction, because it has a fast response mechanism. Moreover, since the signal is not interfered by noise, the complex steps for noise suppression can be skipped, and computing resources can be concentrated on optimizing target imaging, and high-quality imaging results can be generated using current data. For example, when the system determines that the target signal is in signal noise, the execution module 30 will consider that the target signal is blurred and significantly interfered by noise. The system will apply the pre-set layout information of the hydrophone array, which specifically includes the hydrophone spacing, arrangement and depth, to collect acoustic data in the water environment. The acoustic data specifically includes the angle resolution. , distance resolution, main lobe width and side lobe suppression, and draw a sound field distribution map of the water environment based on the acoustic data. The imaging quality of the target signal is obtained through the sound field distribution map. The imaging quality indicators specifically include the signal-to-noise ratio and the blind area. The imaging quality is substituted into the pre-defined imaging quality indicators to generate the content to be optimized for the target signal; the system can better control the propagation path of the sound wave and reduce the impact of noise by utilizing the layout information of the hydrophone array and the acoustic data, thereby improving the ability to capture the target signal and optimizing the spatial resolution of the imaging. At the same time, according to the sound field distribution map and imaging quality indicators, the blind areas in the imaging process can be effectively identified, and the scope of the blind areas can be reduced by adjusting the layout and parameters of the hydrophone array, thereby enhancing the overall The system can generate the content to be optimized of the target signal by substituting the imaging quality into the predefined imaging quality index, which enables the system to have adaptive adjustment capability, and can dynamically adjust the layout and signal processing parameters of the hydrophone array under different underwater environments and noise conditions, thereby improving the overall detection and imaging capabilities of the system. By analyzing the imaging quality of the signal in the sound field distribution diagram, the system can accurately adjust the array layout according to the spacing, arrangement and depth information of the hydrophone array, thereby ensuring that the target signal can be effectively captured and imaged in various underwater environments. Then, the second judgment module 40 judges whether the content to be optimized of the target signal can improve the imaging resolution of the hydrophone after the optimization is completed, so as to execute the corresponding steps.For example, when the system determines that the content to be optimized of the target signal can improve the imaging resolution of the hydrophone after optimization, the system will consider that the optimized hydrophone array layout is effective, and by locking these parameters, the system can maintain this good state in subsequent detection and imaging tasks, and continue to collect target signals using the optimized system configuration to ensure that high-resolution images and signals can be obtained under the new configuration, and continue to dynamically monitor environmental noise, temperature and flow rate factors. On the basis of continuous monitoring data, fine-tuning is performed to further optimize the imaging effect; for example, when the system determines that the content to be optimized of the target signal cannot improve the imaging resolution of the hydrophone after optimization, the second execution module 50 will consider that the optimized hydrophone array layout is invalid, and the system will activate the pre-set phased array to control the phase relationship between each hydrophone, adjust the beam direction in real time according to the phase relationship, and use combined detection of different frequency bands in combination with the phased array to generate acoustic signals of different frequencies through the hydrophone, based on the frequency band characteristics of the acoustic signal, the frequency band characteristics specifically include low-frequency characteristics and high The system can dynamically adjust the beam direction and focus detection on different target areas by activating the phased array and adjusting the phase relationship of the hydrophone in real time. This precise beam control can effectively improve the imaging resolution and make up for the deficiency of the traditional array layout that cannot improve the resolution. At the same time, the weighted fusion of low-frequency and high-frequency features can make full use of the advantages of different frequency signals. Low-frequency signals have better penetration ability and are suitable for long-distance and deep-water target detection, while high-frequency signals have higher resolution and are suitable for fine imaging. Through frequency band fusion, the system can achieve a better balance between imaging range and resolution and obtain clearer and more complete imaging results. The combined detection of different frequency bands is particularly suitable for complex underwater environments. In noisy or multi-path propagation environments, low-frequency signals can help penetrate noise and obstacles, while high-frequency signals can provide fine imaging in a smaller range. By fusing the characteristics of different frequency bands, the system can adapt to changing underwater conditions and improve the imaging quality in complex environments. ;

[0234] In this embodiment, it also includes:

[0235] A focusing module, for selecting a target area from the water environment based on a detection waveform type preset by the hydrophone, switching a transmit beam shape of the hydrophone array according to the target area, and focusing signal energy to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal;

[0236] A third judgment module is used to judge whether the hydrophone can receive the reflected signal in the target area;

[0237] The third execution module is used to generate an arrangement of the hydrophone array if not, detect the automatic gain control parameter of the hydrophone according to the arrangement, identify the ambient noise in the water environment, and dynamically adjust the signal gain of the hydrophone based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array and a mixed array.

[0238] In this embodiment, the system selects a target area from the water environment based on the detection waveform type pre-set by the hydrophone, which specifically includes a linear frequency modulation signal and a pulse signal, and switches the transmit beam shape of the hydrophone array according to the target area. The signal energy is focused on the target area through the transmit beam shape, and then the system determines whether the hydrophone can receive the reflected signal in the target area to execute the corresponding steps; for example, when the system determines that the hydrophone can receive the reflected signal in the target area, the system will consider that the reception of the signal proves that the transmit waveform and beam focusing strategy of the hydrophone array are effective, and the target area has been correctly detected. The system will further detect the target through sound waves of different frequencies, and the low-frequency signal is used to determine the overall outline of the target, and the high-frequency signal is used to capture the details of the target. At the same time, according to the quality of the received signal, the parameters of the transmit waveform are adjusted (such as adjusting the frequency range and duration of the linear frequency modulation or pulse signal), and the beam shape is optimized to improve the signal focusing effect and target resolution. If the target is dynamic, the system will start the automatic tracking algorithm, adjust the transmit beam direction according to the real-time signal, and continuously track the moving trajectory of the target; for example, when the system determines that the hydrophone cannot receive the reflected signal in the target area, At this time, the system will think that the target area cannot be correctly detected, and the system will generate an arrangement of the hydrophone array, which specifically includes non-uniform array, sparse array and hybrid array. According to different arrangement methods, the automatic gain control parameters of the hydrophone are detected, the environmental noise in the water environment is identified, and the signal gain of the hydrophone is dynamically adjusted based on the environmental noise; the system adopts non-uniform array, sparse array or hybrid array, and different arrangement methods can change the spacing and angle between the hydrophones, avoid the spatial resolution blind area caused by uniform arrangement, increase the detection coverage, and adjust the signal gain of the hydrophone according to the environmental noise, dynamically adapt to different underwater environmental noise levels, when the noise is large, the system can automatically enhance the signal gain to amplify the reflected signal, and when the noise is small, reduce the gain to prevent signal oversaturation. This adaptive control mechanism improves the detection accuracy of the system in complex environments, and by identifying the noise characteristics in the water environment, the system can adjust the parameters of the hydrophone for different noise types (such as ship noise, marine biological noise, natural water flow noise). This dynamic adjustment based on noise optimizes the detection performance, so that the system can adaptively adjust and maintain a high detection efficiency in various complex underwater conditions.

[0239] In this embodiment, the execution module further includes:

[0240] an acquisition unit, configured to calculate the relative distance between the hydrophone and the target signal based on preset phase information, extract the scattering intensity of the target signal from the sound field distribution diagram, and acquire the signal characteristics of the target signal;

[0241] A judging unit, used to judge whether the signal characteristic exceeds a preset threshold;

[0242] An execution unit is used to, if yes, use a preset time window sliding to detect the continuity of the target signal, track the moving trajectory of the target signal in real time in the sound field distribution diagram according to the continuity, generate the motion process of the target signal according to the moving trajectory, and draw an imaging quality fluctuation diagram of the target signal through the motion process.

[0243] In this embodiment, the system calculates the relative distance between the hydrophone and the target signal based on the pre-set phase information, extracts the scattering intensity of the target signal from the sound field distribution diagram, obtains the signal characteristics of the target signal, and then the system determines whether the signal characteristics exceed the pre-set threshold value to execute the corresponding steps; for example, when the system determines that the signal characteristics of the target signal do not exceed the pre-set threshold value, the system will consider that the current hydrophone array parameters or detection waveform settings fail to effectively capture the target signal, and the detection effect is not ideal. The system will use signal enhancement technology, such as coherent accumulation and automatic gain control, to amplify the strength of the target signal so that the signal characteristics can exceed the pre-set threshold value. When the preset threshold is exceeded, the beam shape or emission angle of the hydrophone array is adjusted to refocus the signal energy to the target area, increase the intensity of the signal echo, and switch to other frequency bands or adjust the detection waveform (such as linear frequency modulation signal or pulse signal) according to the characteristics of the target to optimize the detection effect and enhance the ability to capture the target. In addition, according to the environmental noise conditions in the target area, the signal gain of the hydrophone is dynamically adjusted to amplify the target signal strength without excessive interference from noise. For example, when the system determines that the signal characteristics of the target signal exceed the preset threshold, the system will consider that the current hydrophone array can effectively capture the target signal, and the system will automatically detect the target signal. The system will use a preset time window sliding to detect the continuity of the target signal, and based on the continuity, track the moving trajectory of the target signal in real time on the sound field distribution map, generate the movement process of the target signal based on the movement trajectory, and draw the imaging quality fluctuation map of the target signal through the movement process; by using the time window sliding to detect the continuity of the target signal, the system can dynamically monitor the changes of the target signal over a period of time to ensure the accuracy of the tracking results, which helps to maintain stable tracking of the target, especially when the target moves quickly or the signal strength fluctuates, to ensure that the signal will not be suddenly interrupted. At the same time, through the sound field distribution map, the system can draw in real time according to the continuity of the target signal. The moving trajectory of the target, the dynamically generated moving trajectory not only helps to track the target position in real time, but also can reveal the target's movement mode (such as straight line, curve, acceleration, etc.), and according to the movement process of the target signal, the system can draw an imaging quality fluctuation diagram to reflect the changes in imaging quality in real time. This visual chart can clearly show the changing trends of imaging quality such as signal strength, clarity and signal-to-noise ratio over time and target movement, and the imaging quality fluctuation diagram can be used to identify time periods or areas with poor imaging quality, helping the system to adjust detection parameters in time, such as the beam shape of the hydrophone array, frequency band switching and gain control, to improve the detection accuracy of the target area.

[0244] In this embodiment, the second execution module further includes:

[0245] A second acquisition unit is used to activate a preset multi-channel synchronous sampler based on a preset signal sampling channel of the hydrophone array, collect signals of different frequency bands using a unified sampling frequency through the signal sampling channel, and acquire acoustic signals of different frequencies;

[0246] A second judgment unit, used to judge whether the acoustic signal detects a preset time delay feature;

[0247] The second execution unit is used to, if yes, use a preset cross-correlation method to calculate the similarity of the acoustic signal in different frequency bands, identify the time difference of the acoustic signal according to the similarity, and correct the acoustic signal through a preset time shift operation to align the acoustic signal in different frequency bands.

[0248] In this embodiment, the system activates the pre-set multi-channel synchronous samplers through the signal sampling channel based on the pre-set signal sampling channel of the hydrophone array, applies a unified sampling frequency to collect signals of different frequency bands, and obtains acoustic signals of different frequencies. Then the system determines whether these acoustic signals detect the pre-set time delay characteristics to execute the corresponding steps; for example, when the system determines that the acoustic signals of different frequencies do not detect the pre-set time delay characteristics, the system will believe that there may be errors in the propagation path of the signal, and the system will adjust the parameters of the signal sampling channel, such as increasing the sampling frequency and increasing the sampling time. Or improve the sampling resolution of the signal to ensure that the time delay feature is more obvious in the data, and enable noise suppression and filtering technology to remove environmental noise and extract clean acoustic signals to improve the detection capability of time delay features. Re-evaluate the propagation path of the sound wave according to the current water environment conditions (such as sound speed distribution, obstacle location, etc.). If the problem lies in the unreasonable array layout (such as array element spacing or arrangement), the system can optimize the delay detection effect by changing the array configuration, such as using sparse or mixed arrays; for example, when the system determines that acoustic signals of different frequencies detect a preset delay feature, At this time, the system will think that there is no error in the propagation path of the signal. The system will use the pre-set cross-correlation method to calculate the similarity of acoustic signals in different frequency bands, identify the time difference of acoustic signals based on the similarity, and correct the acoustic signals through the pre-set time shift operation to align the acoustic signals in different frequency bands. Since acoustic signals in different frequency bands have different characteristics, low-frequency signals often have stronger penetration, while high-frequency signals provide better spatial resolution. By correcting the time difference, the signals in different frequency bands are aligned in time sequence, which helps the system to better integrate these signal features, thereby improving the imaging accuracy and target recognition ability. At the same time, the similarity of signals in different frequency bands is calculated, and the arrival time difference of the signals is accurately aligned through time shift correction, which directly improves the accuracy of target positioning. Especially in complex underwater acoustic environments, the alignment of multi-band signals is crucial for accurately determining the position and motion trajectory of the target. After the acoustic signal is time-shifted, the system can suppress noise more effectively. The aligned signals have higher correlation in different frequency bands, which can make it easier for the system to identify the difference between target signals and noise, thereby improving the signal-to-noise ratio and reducing the interference of noise on signal detection and processing.

[0249] In this embodiment, it also includes:

[0250] Based on the received signal of the hydrophone, detecting the phase difference of the received signal, performing spectrum phase analysis on the received signal according to the phase difference, and acquiring phase difference data of the hydrophone;

[0251] Determining whether the phase difference data matches a preset phase deviation value;

[0252] If not, then based on the phase compensation type of the phase difference data, a phase compensation formula is applied to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on the phase adjustment amount, wherein the phase compensation type specifically includes linear phase compensation and nonlinear phase compensation.

[0253] In this embodiment, the system detects the phase difference of the received signal based on the received signal of the hydrophone, performs spectral phase analysis on the received signal according to different phase differences, obtains the phase difference data of the hydrophone, and then the system determines whether these phase difference data match the preset phase deviation value to execute the corresponding steps; for example, when the system determines that the phase difference data of the hydrophone can match the preset phase deviation value, the system will consider that the signal has not been significantly interfered with during the propagation process, and the propagation path is relatively stable, the system will record and analyze the current signal quality, save the matched phase difference data and related signal characteristics (such as amplitude, frequency, etc.) to the database for subsequent analysis and reference, and draw a sound field distribution map of the water environment based on the phase difference and other signal characteristics to help visualize the acoustic characteristics and target position, and adjust the detection parameters in real time according to the current environment and target status, such as the layout or sampling frequency of the hydrophone array, to improve the detection efficiency; for example, when the system determines that the phase difference data of the hydrophone cannot match the preset phase deviation value, the system will consider that the signal is interfered with during propagation, and the system will Phase compensation type of phase difference data, which specifically includes linear phase compensation and nonlinear phase compensation. The phase compensation formula is used to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on different phase adjustment amounts. Through phase compensation, the system can correct the phase deviation caused by environmental interference (such as water flow, clutter, temperature gradient, etc.). This correction can significantly restore the original quality of the signal, improve the signal-to-noise ratio, and make the received signal clearer and more stable, providing a good foundation for subsequent signal processing. At the same time, through real-time updated phase compensation, signal processing algorithms (such as beamforming, filtering, and feature extraction, etc.) can run on the basis of more accurate signals, reduce processing complexity, improve processing efficiency, and enhance the accuracy of processing results, making subsequent analysis more reliable. Based on real-time feedback of the phase adjustment amount, the system can intelligently adjust the detection strategy, such as the system can change the configuration of the hydrophone array, the signal acquisition frequency or the sampling method, thereby improving the detection efficiency under specific environmental conditions. This dynamic adaptability enables the system to respond flexibly when facing different water environments.

[0254] In this embodiment, the judgment module further includes:

[0255] A generating unit, configured to collect the target echo signal at the same target position at least twice based on the target echo signal detected by the preset receiver through the water environment, and to perform coherent accumulation on the target echo signal to generate a signal-to-noise ratio of the target echo signal;

[0256] A third judging unit, used to judge whether the signal-to-noise ratio reaches a preset decibel value;

[0257] The third execution unit is used to draw a beam center direction according to the estimated position and movement direction of the target signal, and dynamically adjust the signal phase received by the hydrophone according to the beam center direction so that the signal phase is coherently superimposed along the beam center direction.

[0258] In this embodiment, the system collects the target echo signals at the same target position at least twice based on the target echo signals monitored by the water environment by the pre-set receiver, and performs coherent accumulation on these target echo signals to generate the signal-to-noise ratio of the target echo signals, and then the system determines whether these signal-to-noise ratios reach the preset decibel value to execute the corresponding steps; for example, when the system determines that the signal-to-noise ratio of the target echo signal cannot reach the preset decibel value, the system will consider that the target echo signal is seriously interfered by noise, which may cause inaccurate or completely unrecognizable target detection. The system will reconfigure the hydrophone array according to the environmental noise situation, use an array configuration that is more suitable for the current water environment, such as a sparse array or a hybrid array, and dynamically adjust the filtering parameters according to the real-time signal characteristics to enhance the target signal, extract the effective target echo signal, and analyze the possible interference sources to confirm the cause of the low signal-to-noise ratio. If the current detection waveform is not suitable, different signal types can be tried, such as using a linear frequency modulation signal to better handle long-distance detection and improve distance resolution, such as using a pulse signal at a target distance. Provide a higher signal-to-noise ratio when the distance is closer; for example, when the system determines that the signal-to-noise ratio of the target echo signal can reach the preset decibel value, the system will consider that the target detection can be accurately identified, and the system will draw the beam center direction according to the pre-estimated position and movement direction of the target signal, and dynamically adjust the signal phase received by the hydrophone according to the beam center direction, so that the signal phase is coherently superimposed along the beam center direction; through coherent superposition, the system can enhance the strength of the target signal, thereby improving the overall signal-to-noise ratio, making the target echo signal more obvious, easy to identify and analyze, and dynamically adjust the signal phase received by the hydrophone to make it coherently superimposed along the beam center direction, which helps to concentrate signal energy, significantly improve the accuracy of target detection, and reduce position errors. The beam center direction is drawn based on the pre-estimated position and movement direction of the target signal to ensure that the system can adjust in real time, thereby quickly adapting to the movement of the target, which is very important for dynamic monitoring. By accurately controlling the phase of the hydrophone, the system can use its resources more effectively and focus signal processing on the most relevant direction, thereby improving detection efficiency and reducing unnecessary interference.

[0259] In this embodiment, the identification module further includes:

[0260] An extraction unit, used for applying a preset wavelet transform to perform wavelet decomposition on the imaging data, decomposing the target signal into component results of different frequencies and time scales, and extracting each scale coefficient from the component results, wherein each scale coefficient specifically includes energy, mean value and standard deviation;

[0261] A fourth judgment unit, used to judge whether the scale coefficients can be combined into a preset overall feature vector;

[0262] The fourth execution unit is used to classify the overall feature vector if possible, classify and identify the target signal, reconstruct the extracted features of the overall feature vector through a preset inverse wavelet transform, and verify the reflection information of the extracted features on the target signal.

[0263] In this embodiment, the system applies a pre-set wavelet transform to perform wavelet decomposition on the imaging data, decomposes the target signal into component results of different frequencies and time scales, extracts each scale coefficient from the component results, and each scale coefficient specifically includes energy, mean and standard deviation, and then the system determines whether these scale coefficients can be combined into a pre-set overall feature vector to execute the corresponding steps; for example, when the system determines that the scale coefficients cannot be combined into a pre-set overall feature vector, the system will consider that it means that the characteristics of the target signal are insufficient for effective identification or classification, and the system will adjust the mother wavelet type and decomposition layer number of the wavelet transform to better adapt to the frequency characteristics of the target signal, select a suitable time scale to capture the transient characteristics of the signal, and apply a filter (such as a low-pass or high-pass filter) to remove noise and improve signal quality, and re-collect data to ensure the integrity and accuracy of the signal, monitor and correct potential problems in the data collection process; for example, when the system determines that the scale coefficients can be combined into a pre-set overall feature vector, the system will consider that the target signal is not sufficiently characterized. The characteristics of the signal are sufficient to be recognized and classified by the hydrophone. The system will classify the overall feature vector, classify and identify the target signal, and reconstruct the extracted features of the overall feature vector through a pre-set inverse wavelet transform to verify the information reflected by the extracted features on the target signal. By classifying the overall feature vector, the system can more accurately identify the target signal, reduce the misjudgment rate, and improve the reliability of recognition. Because the overall feature vector integrates multi-scale information, it can more comprehensively reflect the characteristics of the target signal, allowing the classifier to better capture the key features of the target. At the same time, by reconstructing the extracted features through the inverse wavelet transform, it can be directly verified whether these features truly and effectively reflect the essence of the target signal, thereby enhancing the credibility of the system. The reconstruction process also provides feedback for further adjustment and optimization of feature extraction and classification methods, which helps to continuously improve system performance. Moreover, through feature extraction and classification, it can reduce the direct processing of large amounts of raw data, reduce computational complexity, and convert complex target signals into relatively concise feature vectors, making subsequent processing, storage, and analysis more efficient.

[0264] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-resolution imaging method for a hydrophone array, characterized in that: The following steps are involved: Based on the pre-collected imaging data, identifying a preset target signal from the imaging data; Determining whether the target signal is in signal noise; If so, the preset layout information of the hydrophone array is applied to collect acoustic data in the water environment, a sound field distribution map of the water environment is drawn according to the acoustic data, the imaging quality of the target signal is obtained through the sound field distribution map, the imaging quality is substituted into the predefined imaging quality index, and the content to be optimized of the target signal is generated, wherein the layout information specifically includes the hydrophone spacing, arrangement mode and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area; Determining whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization; If not, activate the preset phased array to control the phase relationship between each hydrophone, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophones, and based on the frequency band characteristics of the acoustic signal, perform weighted fusion on the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

2. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: Before the step of applying the preset layout information of the hydrophone array to collect acoustic data in the water environment, the method further includes: Based on the detection waveform type preset by the hydrophone, a target area is selected from the water environment, and the transmit beam shape of the hydrophone array is switched according to the target area, and the signal energy is focused to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal; determining whether the hydrophone can receive a reflected signal in the target area; If not, an arrangement of the hydrophone array is generated, an automatic gain control parameter of the hydrophone is detected according to the arrangement, the ambient noise in the water environment is identified, and the signal gain of the hydrophone is dynamically adjusted based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array and a mixed array.

3. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: The step of drawing a sound field distribution map of the water environment according to the acoustic data and obtaining the imaging quality of the target signal through the sound field distribution map includes: Calculating the relative distance between the hydrophone and the target signal based on preset phase information, extracting the scattering intensity of the target signal from the sound field distribution diagram, and acquiring the signal characteristics of the target signal; Determining whether the signal characteristic exceeds a preset threshold; If so, a preset time window sliding detection is used to detect the continuity of the target signal, and the moving trajectory of the target signal is tracked in real time in the sound field distribution diagram according to the continuity, and the motion process of the target signal is generated according to the moving trajectory, and the imaging quality fluctuation diagram of the target signal is drawn through the motion process.

4. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: The step of combining the phased array with different frequency bands to generate acoustic signals of different frequencies through the hydrophone includes: Based on the preset signal sampling channel of the hydrophone array, activating the preset multi-channel synchronous sampler to collect signals of different frequency bands through the signal sampling channel with a unified sampling frequency to obtain acoustic signals of different frequencies; Determining whether a preset time delay feature is detected in the acoustic signal; If so, a preset cross-correlation method is used to calculate the similarity of the acoustic signal in different frequency bands, the time difference of the acoustic signal is identified according to the similarity, and the acoustic signal is corrected through a preset time shift operation to align the acoustic signal in different frequency bands.

5. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: After the step of activating the preset phased array to control the phase relationship between the hydrophones and adjusting the beam direction in real time according to the phase relationship, the method further includes: Based on the received signal of the hydrophone, detecting the phase difference of the received signal, performing spectrum phase analysis on the received signal according to the phase difference, and acquiring phase difference data of the hydrophone; Determining whether the phase difference data matches a preset phase deviation value; If not, then based on the phase compensation type of the phase difference data, a phase compensation formula is applied to calculate the phase adjustment amount of the hydrophone, and the phase compensation amount of the hydrophone in the water environment is dynamically updated based on the phase adjustment amount, wherein the phase compensation type specifically includes linear phase compensation and nonlinear phase compensation.

6. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: The step of determining whether the target signal is in signal noise further includes: Based on the target echo signal detected by the preset receiver through the water environment, the target echo signal at the same target position is collected at least twice, and the target echo signal is coherently accumulated to generate a signal-to-noise ratio of the target echo signal; Determining whether the signal-to-noise ratio reaches a preset decibel value; If yes, a beam center direction is drawn according to the estimated position and movement direction of the target signal, and the phase of the signal received by the hydrophone is dynamically adjusted according to the beam center direction so that the signal phase is coherently superimposed along the beam center direction.

7. The high-resolution imaging method of a hydrophone array according to claim 1, characterized in that: The step of identifying a preset target signal from the imaging data based on the pre-collected imaging data further includes: Applying a preset wavelet transform to perform wavelet decomposition on the imaging data, decomposing the target signal into component results of different frequencies and time scales, and extracting each scale coefficient from the component results, wherein each scale coefficient specifically includes energy, mean value and standard deviation; Determining whether the scale coefficients can be combined into a preset overall feature vector; If possible, the overall feature vector is classified, the target signal is identified by classification, the extracted features of the overall feature vector are reconstructed through a preset inverse wavelet transform, and the reflection information of the extracted features on the target signal is verified.

8. A high-resolution imaging system of a hydrophone array, characterized in that: include: An identification module, configured to identify a preset target signal from the imaging data based on the pre-collected imaging data; A judging module, used for judging whether the target signal is in signal noise; an execution module, for, if yes, applying the preset layout information of the hydrophone array, collecting acoustic data in the water environment, drawing a sound field distribution map of the water environment according to the acoustic data, obtaining the imaging quality of the target signal through the sound field distribution map, substituting the imaging quality into a predefined imaging quality index, and generating content to be optimized for the target signal, wherein the layout information specifically includes the hydrophone spacing, arrangement and depth, the acoustic data specifically includes the angular resolution, distance resolution and main lobe width and side lobe suppression, and the imaging quality index specifically includes the signal-to-noise ratio and the blind area; A second judgment module is used to judge whether the content to be optimized can improve the imaging resolution of the hydrophone after optimization; The second execution module is used to activate the preset phased array to control the phase relationship between each hydrophone if it is not possible, adjust the beam direction in real time according to the phase relationship, combine the phased array to use combined detection of different frequency bands, generate acoustic signals of different frequencies through the hydrophone, and based on the frequency band characteristics of the acoustic signal, weightedly fuse the frequency band characteristics to obtain a fused imaging result, wherein the frequency band characteristics specifically include low-frequency characteristics and high-frequency characteristics.

9. The high-resolution imaging system of the hydrophone array according to claim 8, characterized in that: Also includes: A focusing module, for selecting a target area from the water environment based on a detection waveform type preset by the hydrophone, switching a transmit beam shape of the hydrophone array according to the target area, and focusing signal energy to the target area through the transmit beam shape, wherein the detection waveform type specifically includes a linear frequency modulation signal and a pulse signal; A third judgment module is used to judge whether the hydrophone can receive the reflected signal in the target area; The third execution module is used to generate an arrangement of the hydrophone array if not, detect the automatic gain control parameter of the hydrophone according to the arrangement, identify the ambient noise in the water environment, and dynamically adjust the signal gain of the hydrophone based on the ambient noise, wherein the arrangement specifically includes a non-uniform array, a sparse array and a mixed array.

10. The high-resolution imaging system of the hydrophone array according to claim 8, characterized in that: The execution module also includes: an acquisition unit, configured to calculate the relative distance between the hydrophone and the target signal based on preset phase information, extract the scattering intensity of the target signal from the sound field distribution diagram, and acquire the signal characteristics of the target signal; A judging unit, used to judge whether the signal characteristic exceeds a preset threshold; An execution unit is used to, if yes, use a preset time window sliding to detect the continuity of the target signal, track the moving trajectory of the target signal in real time in the sound field distribution diagram according to the continuity, generate the motion process of the target signal according to the moving trajectory, and draw an imaging quality fluctuation diagram of the target signal through the motion process.

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