A self-stabilized beamforming method for fish-finding sonar

Abstract

The present application relates to the technical field of underwater acoustic detection, and particularly relates to a self-stabilized beam forming method for fish detection sonar. The method comprises: collecting multi-source signals through a sonar array, an attitude sensor and an environment sensor; calculating a beam pointing angle compensation amount based on attitude data to adjust the phase weight of the array element, so as to offset the influence of the hull sway; estimating the sound speed value through a layered sound speed model using temperature and salinity data, and correcting the sound wave propagation time difference; calculating the array element weight and forming a directional beam using an improved adaptive beam forming algorithm, which introduces a momentum factor to accelerate convergence and sets a sidelobe suppression constraint; finally, evaluating the quality of the beam output signal, and feeding back the adjustment of the front-end compensation and estimation parameters to realize closed-loop optimization. Through multi-source information fusion and real-time compensation correction, the present application significantly improves the beam pointing stability, target positioning accuracy, and the adaptability and robustness of the system in complex environments.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic detection technology, specifically to a method for self-stabilizing beamforming in fish-finding sonar. Background Technology

[0002] Fish-finding sonar, as an important underwater detection device, is widely used in marine fisheries resource surveys, fishing operation navigation, and aquaculture monitoring. Its core technology lies in receiving underwater acoustic signals through a sensor array and generating a directional beam using beamforming algorithms, thereby achieving effective detection and location of fish targets. Existing technologies for fish-finding sonar mostly employ fixed-weighted beamforming methods or traditional adaptive beamforming algorithms, such as the least mean square algorithm or the minimum variance distortionless response algorithm. These methods adjust the weighting coefficients of each element in the sensor array to achieve high gain in a specific direction for spatial filtering. However, in actual marine environments, the operating platform of fish-finding sonar, such as fishing vessels, is subject to continuous swaying due to factors like wind, waves, and currents. Simultaneously, the uneven vertical distribution of temperature and salinity in the water creates a sound velocity stratification effect. These dynamic interference factors pose a significant challenge to traditional beamforming techniques. Fixed-weighted methods cannot adapt to dynamic environmental changes; their beam directivity shifts significantly during platform swaying, causing target positioning errors. While traditional adaptive algorithms possess a certain degree of environmental adaptability, their weight update convergence speed is often too slow when faced with severe ship swaying or rapid changes in water sound velocity profiles. This makes it difficult to track environmental changes in real time, leading to beam main lobe distortion, increased sidelobe levels, and the target signal being overwhelmed by interference signals. Furthermore, existing technologies typically rely solely on acoustic signal processing, lacking deep integration with attitude sensing data such as inertial measurement units, and are unable to effectively compensate for array geometric deformation caused by platform motion. These technical shortcomings severely limit the detection accuracy and reliability of fish-finding sonar in complex marine environments, making it difficult to meet the high requirements of practical applications in terms of fish school identification accuracy. Therefore, there is an urgent need for a novel beamforming method that can effectively overcome dynamic environmental interference and achieve beam pointing self-stabilization.

[0003] Therefore, the existing technology still needs further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a self-stabilizing beamforming method for fish-finding sonar to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned technical objectives, the present invention provides a method for self-stabilizing beamforming of fish-finding sonar, comprising: S1: Acquire underwater acoustic signals through a sonar sensor array, acquire attitude data of the fish-finding device through an attitude sensor, and collect water environment parameters through an environmental sensor. S2: Based on the collected attitude data, calculate the compensation amount of the beam pointing angle and adjust the phase weight of each element in the sonar sensor array. S3: Based on the collected water environment parameters, the sound velocity value is estimated using a sound velocity model, and the sound wave propagation time difference is corrected based on the estimated sound velocity value. S4: Using an adaptive beamforming algorithm, based on the compensated phase weights and the corrected acoustic wave propagation time difference, the array element weights are calculated to form a directional beam; S5: Evaluate the quality of the output signal of the directional beam and adjust the parameters in S2 and S3 based on the quality evaluation results.

[0006] Specifically, after S1, there is also a data preprocessing step to preprocess the acquired acoustic signals, attitude data and water environment parameters, wherein the preprocessing includes noise reduction and synchronization alignment.

[0007] Specifically, the attitude data includes pitch angle, roll angle, and yaw angle, and is acquired through an IMU attitude sensor.

[0008] Specifically, S2 includes calculating the spatial position deviation of the sensor array through a coordinate transformation algorithm, and then calculating the compensation amount of the beam pointing angle.

[0009] Specifically, in S3, the sound velocity model is a layered sound velocity model, used to estimate the sound velocity values ​​at different water depths.

[0010] Specifically, the water environment parameters include water temperature and salinity.

[0011] Specifically, S3 also includes using a sound velocity error compensation table to quickly adapt to different water environments.

[0012] Specifically, in S4, the adaptive beamforming algorithm is the Normalized Least Mean Square (NLMS) algorithm.

[0013] Specifically, the NLMS algorithm introduces a momentum factor to accelerate weight updates.

[0014] Specifically, the NLMS algorithm also sets sidelobe suppression constraints to reduce sidelobe levels.

[0015] Beneficial effects: The self-stabilizing beamforming method for fish-finding sonar provided by this invention brings about many significant benefits by deeply fusing multi-source information and introducing a closed-loop optimization mechanism.

[0016] First, this method, by introducing high-frequency attitude sensor data and establishing a precise coordinate transformation model, achieves real-time compensation for spatial position deviations of the sensor array caused by the ship's pitch, roll, and yaw motions. This in-depth attitude linkage correction mechanism effectively counteracts the negative impact of platform sway on beam pointing, ensuring the stability of the beam's main lobe direction. Even under conditions of significant swaying, beam pointing offset is controlled to an extremely low level, thereby fundamentally improving the spatial positioning accuracy of the target.

[0017] Secondly, this invention abandons the traditional average sound velocity model and innovatively adopts a stratified sound velocity estimation model based on real-time water temperature and salinity measurements. This model can accurately depict the vertical variation of sound wave propagation speed in actual water bodies, effectively overcoming beam distortion and positioning errors caused by sound velocity stratification. Through synergy with attitude compensation, the accuracy of target positioning has achieved a qualitative leap.

[0018] Third, at the core beamforming algorithm level, key improvements were made to the traditional adaptive algorithm. A momentum factor was introduced to accelerate the convergence process of weight updates, and sidelobe suppression constraints were set. These improvements enable the algorithm to respond quickly to sudden environmental changes, avoid transient detection failures, and actively suppress sidelobe interference, significantly improving the signal-to-noise ratio and main lobe clarity of the output signal.

[0019] Finally, this invention introduces a feedback optimization closed loop based on beam quality assessment parameters. This system continuously monitors the beamforming effect and dynamically adjusts the compensation and estimation parameters at the front end, forming a self-optimizing intelligent system that greatly enhances the adaptability and robustness of the entire method under different hydrological conditions and motion states.

[0020] In summary, this invention effectively solves the core problems faced by existing technologies in dynamic marine environments, such as beam instability, inaccurate positioning, and poor anti-interference, and comprehensively improves the detection performance, environmental adaptability, and operational reliability of fish-finding sonar. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the self-stabilizing beamforming method for fish-finding sonar provided in a specific embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0023] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0024] Please see Figure 1 This invention provides a method for self-stabilizing beamforming of fish-finding sonar, comprising: S1: Acquire underwater acoustic signals through a sonar sensor array, acquire attitude data of the fish-finding device through an attitude sensor, and acquire water environment parameters through an environmental sensor.

[0025] It should be further explained that this method acquires acoustic signals, attitude data, and aquatic environmental parameters simultaneously via S1. The sonar sensor array adopts a uniform linear array structure, with an optimal number of 32 elements to balance beam resolution and computational complexity. The element spacing is set to half a wavelength to avoid grating lobes. The acoustic signal center frequency is 200kHz, the bandwidth is 50kHz, and the sampling frequency is 500kHz to ensure signal integrity. The attitude sensor uses an IMU (Inertial Measurement Unit), which includes a three-axis gyroscope and a three-axis accelerometer, with a sampling frequency set to 100kHz. The sampling frequency is set at Hz, which effectively captures the typical frequency range of hull rolling (0.1Hz to 10Hz), avoiding distortion caused by undersampling. The environmental sensors include a temperature sensor and a salinity sensor. The temperature sensor uses a PT1000 platinum resistance thermometer with a measurement range of -5°C to 40°C and an accuracy of 0.1°C. The salinity sensor uses a conductivity sensor with a measurement range of 0-40ppt and an accuracy of 0.1ppt. The sampling frequency is set to 10Hz to adapt to the slow changes in aquatic environmental parameters. Data is synchronized via timestamps after acquisition, with synchronization deviation controlled within 10 milliseconds.

[0026] S2: Based on the collected attitude data, calculate the compensation amount of the beam pointing angle and adjust the phase weight of each element in the sonar sensor array.

[0027] It should be further explained that in S2, the spatial position deviation of the sensor array is corrected based on IMU data using a coordinate transformation algorithm. Specifically, the Euler angle rotation order ZYX is used, and the rotation matrix formula is: in, It is a rotation matrix. It is the roll angle (unit: degrees). It is the pitch angle (unit: degrees). It is the heading angle (unit: degrees). It is a rotation matrix about the X-axis. It is a rotation matrix about the Y-axis. It is the rotation matrix around the Z-axis; the formula for calculating the beam pointing angle compensation is: in, It is the beam pointing angle compensation amount (unit: degrees). and It represents the horizontal and vertical components of the array element position deviation (unit: meters), calculated using the rotation matrix and the nominal position vector; the phase weight adjustment formula is: in, It is the phase weight (dimensionless) of the i-th array element. It is the frequency of sound waves (unit: Hz). It is the time delay compensation amount for the i-th array element (unit: seconds).

[0028] S3: Based on the collected water environment parameters, the sound velocity value is estimated using a sound velocity model, and the sound wave propagation time difference is corrected based on the estimated sound velocity value.

[0029] It should be further explained that in S3, the sound velocity model adopts a layered sound velocity model, dividing the water body from the surface to the bottom into several layers, each preferably 1 meter thick. The number of layers is set according to the maximum detectable water depth (e.g., 50 layers correspond to a water depth of 50 meters). The sound velocity is estimated using the Mackenzie formula: in, It is the speed of sound at a water depth of z (unit: meters per second). It is the temperature at a water depth of z (unit: degrees Celsius). This is the salinity at a water depth of z (unit: ppt). The water depth is in meters; the formula for correcting the time difference of sound wave propagation is: in, It is the time difference correction amount (unit: seconds). It is the distance sound waves can travel (unit: meters). It is the average speed of sound (unit: meters per second), with a preferred value of 1500 meters per second.

[0030] S4: Using an adaptive beamforming algorithm, the array element weights are calculated based on the compensated phase weights and the corrected acoustic wave propagation time difference to form a directional beam.

[0031] It should be further noted that in S4, the adaptive beamforming algorithm uses a modified NLMS (Normalized Least Mean Square) algorithm, and the weight update formula is as follows: in, It is the weight vector (dimensionless) at time n. It is the step size factor, with a preferred value of 0.01. This value can balance the convergence speed and steady-state error. The reason for choosing this value is based on the fact that the empirical value is stable in dynamic environments. This is the regularization constant, set to 10^{-6} to prevent division by zero; It is the input signal vector (unit: volts). It is the error signal (unit: volts), and the calculation formula is: , The desired signal (unit: volts); momentum factor The preferred value is 0.9, which accelerates convergence and suppresses oscillations. The rationale for this choice is that experiments show it can improve convergence speed by 40%. The sidelobe suppression constraint is achieved through an optimization problem, with the constraint condition being... for ,in It is a directional vector (dimensionless). The sidelobe region (unit: degrees) is defined as the area beyond 30 degrees to the left and right of the main lobe, and is the sidelobe suppression threshold. The preferred value is 0.1 (corresponding to -20dB), which can effectively reduce interference.

[0032] S5: Evaluate the quality of the output signal of the directional beam and adjust the parameters in S2 and S3 based on the quality evaluation results.

[0033] It should be further explained that in S5, the signal quality evaluation parameters include main lobe width, side lobe level and target signal-to-noise ratio. The main lobe width threshold is set to 3dB with a width not exceeding 5 degrees, the side lobe level threshold is set to -25dB, and the target signal-to-noise ratio threshold is set to 10dB. If the parameters exceed the threshold, the attitude compensation coefficient (such as the angle correction in the rotation matrix) and the parameters in the sound velocity estimation model (such as the layer thickness) are adjusted in reverse to form a closed-loop optimization with an adjustment period of 1 second.

[0034] Understandably, this method effectively improves the stability of beam pointing by multi-source signal fusion and real-time compensation, avoiding positioning errors caused by hull swaying and water flow interference; dynamic sound speed estimation and feedback optimization ensure the adaptability of the algorithm in different water environments, improving the accuracy and reliability of fish school identification.

[0035] Specifically, after S1, there is also a data preprocessing step to preprocess the acquired acoustic signals, attitude data and water environment parameters, wherein the preprocessing includes noise reduction and synchronization alignment.

[0036] It should be further explained that the data preprocessing step first denoises the acoustic signal using a wavelet thresholding denoising algorithm. The preferred wavelet basis function is Db4, the decomposition level is 5, and an adaptive threshold is selected. The calculation formula is as follows: ,in It is the threshold (unit: volts). It is the noise standard deviation (unit: volts). The signal length (dimensionless) is used, and the signal-to-noise ratio is improved by more than 10dB after denoising. Attitude data is smoothed using a Kalman filter algorithm. State variables include angle and angular velocity. The state and observation equations are based on linear models, with the process noise covariance set to 0.001 and the observation noise covariance set to 0.01 to reduce the impact of sensor jitter. Outliers in environmental parameters are removed using the 3σ criterion: the mean and standard deviation of the data are calculated, and data exceeding three times the standard deviation of the mean are considered invalid and replaced by interpolation. Synchronization alignment is achieved through hardware timestamps, ensuring that the acquisition time deviation of acoustic signals, attitude data, and environmental parameters is controlled within 10 milliseconds to guarantee spatiotemporal consistency. Preprocessed data is stored in a circular buffer, preferably 1000 samples in size, for subsequent steps.

[0037] Understandably, the data preprocessing step improves data quality, reduces noise and errors caused by asynchronicity, provides reliable input for subsequent compensation and estimation, and enhances the robustness of the system.

[0038] Specifically, the attitude data includes pitch angle, roll angle, and yaw angle, and is acquired through an IMU attitude sensor.

[0039] It should be further noted that the preferred IMU attitude sensor is the MPU-6050 model, which integrates a three-axis gyroscope and a three-axis accelerometer. The gyroscope range is set to ±250° / second, the accelerometer range is set to ±2g, and the sampling frequency is 100Hz, which can cover the typical frequency range of ship sway. The pitch, roll, and yaw angles are calculated using a sensor fusion algorithm with complementary filtering. The weighting coefficient of the complementary filtering is preferably 0.98, i.e., the angle output is... ,in This is the angle after fusion (unit: degrees). It is the gyroscope angle (unit: degrees). It is angular velocity (unit: degrees per second). It is the sampling interval (unit: seconds). The accelerometer angle (unit: degrees) is selected. The reason for choosing a weighting factor of 0.98 is to balance the short-term accuracy of the gyroscope and the long-term stability of the accelerometer. The attitude angle data output frequency is 100Hz, the accuracy is 0.1 degrees, and it is transmitted to the processing unit through the SPI interface.

[0040] Understandably, using IMU sensors to collect full attitude angle data provides high-precision input for beam pointing compensation, avoiding errors caused by insufficient data from a single sensor.

[0041] Specifically, S2 includes calculating the spatial position deviation of the sensor array through a coordinate transformation algorithm, and then calculating the compensation amount of the beam pointing angle.

[0042] It should be further explained that the coordinate transformation algorithm is based on the Euler angle rotation order ZYX, which converts the global coordinate system into the sensor's local coordinate system. The rotation matrix formula is as described above. The spatial position deviation is calculated by multiplying the nominal position vector of the array element by the rotation matrix. The deviation vector formula is: in, It is the positional deviation vector (unit: meters). It is the nominal position vector of the array element (unit: meters). It is a rotation matrix (dimensionless); the beam pointing angle compensation is calculated by projecting the deviation vector onto the beam direction, and the compensation formula is: in, It is the horizontal plane compensation angle (unit: degrees). and These are the X and Y components of the deviation vector (unit: meters); an attitude error model is established, and the model parameters are calibrated experimentally, for example, by measuring the deviation on a static platform, correcting the beam pointing direction in real time, and updating the frequency to 100Hz.

[0043] Understandably, the coordinate transformation algorithm accurately quantifies the impact of attitude changes on array geometry, and the beam pointing deviation is significantly reduced after compensation, thus improving target positioning accuracy.

[0044] Specifically, in S3, the sound velocity model is a layered sound velocity model, used to estimate the sound velocity values ​​at different water depths.

[0045] It should be further explained that the layered sound velocity model divides the water body from the surface to the bottom into several layers, with each layer preferably 1 meter thick. This thickness balances calculation accuracy and real-time performance. The number of layers is set according to the maximum detectable water depth; for example, a water depth of 50 meters is divided into 50 layers. The sound velocity of each layer is calculated based on the temperature and salinity data at the center of that layer, using the Mackenzie formula as described above. The sound velocity value is updated in real time at a frequency of 1 Hz to adapt to changes in the water environment. The sound velocity value is used to correct for the sound wave propagation time difference, and the time difference correction formula is as described above. At the same time, a sound velocity error compensation table is established. This table is a two-dimensional lookup table, with the index being water temperature and salinity. The temperature range is 0-30°C with a resolution of 1°C, and the salinity range is 0-40 ppt with a resolution of 1 ppt. The table values ​​are the sound velocity correction amount (unit: m / s), constructed using historical data, and the lookup update cycle is 1 hour.

[0046] Understandably, the stratified sound velocity model avoids the errors of the traditional average sound velocity and is especially suitable for water bodies with obvious sound velocity stratification, thus improving beamforming accuracy.

[0047] Specifically, the water environment parameters include water temperature and salinity.

[0048] It should be further explained that the water temperature is collected by a PT1000 temperature sensor, which is installed at the bottom of the fish finder. The measurement range is -5°C to 40°C, with an accuracy of 0.1°C and a sampling frequency of 10Hz. The salinity is collected by a conductivity sensor, with a measurement range of 0-40ppt, an accuracy of 0.1ppt, and a sampling frequency of 10Hz. After data collection, the data is converted into actual values ​​through a calibration curve, which is based on standard solution calibration. Before the temperature and salinity data are used in the sound velocity calculation, a moving average filter is applied, with a window length of 10 seconds preferred to smooth short-term fluctuations.

[0049] Understandably, temperature and salinity data serve as a crucial input for sound speed calculation, ensuring the accuracy of sound speed estimation and thus reducing beam distortion.

[0050] Specifically, S3 also includes using a sound velocity error compensation table to quickly adapt to different water environments.

[0051] It should be further explained that the sound speed error compensation table is constructed using historical experimental data. The table structure is a two-dimensional array, with row indices representing temperature values ​​(unit: degrees Celsius), column indices representing salinity values ​​(unit: ppt), and array values ​​representing sound speed correction amounts. (Unit: m / s); Temperature range covers 0°C to 30°C, with a step size of 1°C; Salinity range covers 0 ppt to 40 ppt, with a step size of 1 ppt; During real-time estimation, the correction amount is obtained by linear interpolation based on the current water temperature and salinity values, and the final sound velocity formula is: in, This is the corrected speed of sound (unit: meters per second). It is a model that estimates the speed of sound (unit: meters per second). This is the table lookup correction amount (unit: meters / second); the table update cycle is 24 hours, and it learns and adapts to long-term changes through backend data.

[0052] Understandably, the sound velocity error compensation table provides a fast adaptation mechanism, reduces the amount of real-time calculation, and improves algorithm efficiency, making it especially suitable for embedded devices.

[0053] Specifically, in S4, the adaptive beamforming algorithm is the Normalized Least Mean Square (NLMS) algorithm.

[0054] It should be further explained that the weight update formula of the NLMS algorithm is as described above, where the step size factor... The preferred value is 0.01, chosen because it strikes a balance between convergence speed and stability. Experiments show that excessively large step sizes lead to oscillations, while excessively small step sizes result in slow convergence; regularization constant. The input signal norm is set to 10^{-6} to prevent numerical instability caused by an excessively small input signal norm; the algorithm iteration frequency is 1000Hz to ensure real-time beamforming; the desired signal... Generated using training sequences or reference beams, with initial weights set to uniform weights.

[0055] Understandably, the NLMS algorithm is computationally simple, has good stability, is suitable for dynamic environments, and can effectively form directional beams.

[0056] Specifically, the NLMS algorithm introduces a momentum factor to accelerate weight updates.

[0057] It should be further explained that the momentum factor The preferred value is 0.9, which was determined experimentally. The rationale for this choice is that a momentum factor that is too small (e.g., 0.5) does not produce a significant acceleration effect, while a factor that is too large (e.g., 0.99) easily introduces noise. The momentum term in the weight update formula... It retains the historical update direction, accelerating convergence; when applying the momentum factor, the weight update step size is adaptively adjusted, improving the convergence speed by 40% when the environment changes abruptly.

[0058] Understandably, the momentum factor speeds up weight updates, enabling the algorithm to quickly adapt to sudden environmental changes and reducing brief detection failures.

[0059] Specifically, the NLMS algorithm also sets sidelobe suppression constraints to reduce sidelobe levels.

[0060] It should be further explained that the sidelobe suppression constraint is achieved through an optimization problem. The optimization objective is to minimize the output power (subjectto), the main lobe gain is 1, and the sidelobe gain constraint. Specifically, the Lagrange multiplier method is used to solve this problem, and the constraint conditions are as follows: for ,in This is the sidelobe suppression threshold, with a preferred value of 0.1 (corresponding to -20dB). The rationale for this choice is that this threshold can effectively suppress interference without significantly affecting the main lobe; sidelobe region Defined as the region beyond 30 degrees to the left and right of the main lobe direction (unit: degrees); the weights obtained from the solution are directly used for beamforming.

[0061] Understandably, sidelobe suppression constraints reduce the impact of interference signals, improve the clarity of the main lobe, and enhance the accuracy of fish swarm identification.

[0062] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the self-stabilizing beamforming method for fish-finding sonar. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0063] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0064] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0065] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0066] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for self-stabilizing beamforming in fish-finding sonar, characterized in that, Includes the following steps: S1. Acquire underwater acoustic signals through a sonar sensor array, acquire attitude data of the fish-finding device through an attitude sensor, and collect water environment parameters through an environmental sensor. S2. Based on the collected attitude data, calculate the compensation amount of the beam pointing angle and adjust the phase weight of each element in the sonar sensor array. S3. Based on the collected water environment parameters, the sound velocity value is estimated using a sound velocity model, and the sound wave propagation time difference is corrected based on the estimated sound velocity value. S4. Using an adaptive beamforming algorithm, the array element weights are calculated based on the compensated phase weights and the corrected acoustic wave propagation time difference to form a directional beam. S5. Evaluate the quality of the output signal of the directional beam and adjust the parameters in S2 and S3 based on the quality evaluation results.

2. The self-stabilizing beamforming method for fish-finding sonar according to claim 1, characterized in that, Following S1, a data preprocessing step is also included to preprocess the acquired acoustic signals, attitude data, and water environment parameters, wherein the preprocessing includes noise reduction and synchronization alignment.

3. The self-stabilizing beamforming method for fish-finding sonar according to claim 2, characterized in that, The attitude data includes pitch angle, roll angle, and yaw angle, and is acquired through an IMU attitude sensor.

4. The self-stabilizing beamforming method for fish-finding sonar according to claim 3, characterized in that, S2 includes calculating the spatial position deviation of the sensor array through a coordinate transformation algorithm, and then calculating the compensation amount of the beam pointing angle.

5. The self-stabilizing beamforming method for fish-finding sonar according to claim 1, characterized in that, In S3, the sound velocity model is a layered sound velocity model, used to estimate the sound velocity values ​​at different water depths.

6. The self-stabilizing beamforming method for fish-finding sonar according to claim 5, characterized in that, The water environment parameters include water temperature and salinity.

7. The self-stabilizing beamforming method for fish-finding sonar according to claim 6, characterized in that, The S3 also includes using a sound velocity error compensation table to quickly adapt to different water environments.

8. The self-stabilizing beamforming method for fish-finding sonar according to claim 1, characterized in that, In S4, the adaptive beamforming algorithm is the Normalized Least Mean Square (NLMS) algorithm.

9. The self-stabilizing beamforming method for fish-finding sonar according to claim 8, characterized in that, The NLMS algorithm introduces a momentum factor to accelerate weight updates.

10. The self-stabilizing beamforming method for fish-finding sonar according to claim 9, characterized in that, The NLMS algorithm also sets sidelobe suppression constraints to reduce sidelobe levels.

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