An array signal processing simulation and evaluation system for underwater sonar systems

Abstract

This invention discloses a simulation and evaluation system for array signal processing in underwater sonar systems, belonging to the field of underwater sonar technology. The system includes: an underwater acoustic channel simulation module, used to generate an underwater channel model including ray bending and multipath effects and the channel response of each array element based on input marine environmental parameters using a ray tracing algorithm; a signal synthesis module, used to synthesize simulated multi-channel received signals of the array based on array parameters, target parameters, and channel responses; an algorithm processing module, integrating multiple underwater acoustic signal processing algorithms to process the received signals and output beamforming or spatial spectrum estimation results; and a performance evaluation module, which visualizes the processing results and calculates multi-dimensional performance indicators. This invention significantly improves the realism of the simulation, the completeness of the test scenario, and the guiding nature of the evaluation conclusions through high-fidelity physical modeling and closed-loop evaluation, effectively supporting sonar system design, algorithm verification, and performance prediction.

Description

Technical Field

[0001] This invention belongs to the field of underwater sonar technology, specifically relating to an array signal processing simulation and evaluation system for underwater sonar systems. Background Technology

[0002] Underwater sonar systems are core equipment that utilizes sound waves for detection, location, identification, and communication in water. Array signal processing technology, by utilizing the spatiotemporal information of signals received by multiple hydrophones (array elements), can effectively improve the detection range, azimuth resolution, interference suppression capability, and target identification performance of sonar systems. However, the underwater acoustic environment is extremely complex. Sound wave propagation is affected by factors such as the sound velocity profile (which varies with depth), the sea surface and seabed boundaries, and water inhomogeneity, resulting in significant sound ray bending, multipath effects, signal fading, and complex environmental noise and reverberation. These factors often lead to a significant discrepancy between the performance of array signal processing algorithms in actual waters and the results of theoretical analysis or simulations based on simple assumptions.

[0003] Therefore, in the design, algorithm research, and performance prediction stages of sonar systems, a simulation and evaluation system capable of realistically simulating underwater physical channels and objectively assessing the performance of processing algorithms is crucial. However, existing array signal processing simulation schemes or systems have many limitations, mainly manifested in: Oversimplification in underwater channel modeling leads to distorted simulation environments: Most existing simulation systems, when simulating underwater acoustic channels, often employ idealized homogeneous medium assumptions and simplistic spherical or cylindrical wave attenuation models, neglecting ray bending and time- and space-varying multipath structures. For example, failing to incorporate real sound velocity profile data into the sound wave propagation path calculation results in simulated wave arrival directions that do not match reality; inadequate modeling of complex reflections and scattering caused by sea surface wave reflections, seabed topography, and sediment results in a lack of physically realistic multipath arrival signals and reverberation interference in the simulated signal. This distorted simulation environment cannot effectively verify the robustness of algorithms in real, complex water environments.

[0004] Interference and noise simulations are incomplete, particularly lacking physical-level modeling of coherent interference. In real underwater environments, in addition to ambient noise, there are strong coherent interference sources such as hostile sonar and vehicle-radiated noise. Existing simulation systems typically simplify interference to statistically independent additive white Gaussian noise or simply assign it a certain directionality. This approach ignores the physical changes in waveform, amplitude, phase, and spatial correlation of coherent interference as it propagates through complex underwater acoustic channels before reaching the array. The lack of coherent interference simulations based on physical propagation models makes it impossible to effectively evaluate the anti-interference performance of the array system and processing algorithms in real electronic warfare or complex acoustic environments.

[0005] The simulation and evaluation processes are disconnected, with evaluation dimensions being singular and failing to form a closed loop: Many simulation tools focus on signal generation or performance demonstrations of specific algorithms, lacking a systematic performance evaluation module. Evaluations are often limited to simple metrics such as signal-to-noise ratio gain and main lobe width, failing to closely integrate with sonar application requirements, such as detection probability in dynamic reverberation backgrounds, multi-target tracking continuity, and robustness of positioning accuracy under environmental parameter mismatches. Furthermore, there is a lack of automated sensitivity analysis links between simulation parameter settings (such as environment, array, and target) and the final performance evaluation results, making it difficult to guide system design optimization.

[0006] The simulations do not adequately consider the dynamics and complexity of actual deployments: Existing simulations often assume that the sonar platform is stationary and the array geometry is fixed. However, real-world sonar systems are often deployed on moving platforms (such as ships, submarines, and towed vehicles), and the platform's motion causes time-varying array manifolds, Doppler effects, and signal coherence loss. Furthermore, real-world scenarios are often dynamic environments with multiple targets. Existing simulation systems lack sufficient integration of these factors, making it difficult to evaluate the performance of motion compensation algorithms and the system's ability to handle complex situations such as multi-target intersections, mergers, and splits.

[0007] In summary, current technologies lack an integrated simulation and evaluation system capable of generating realistic array signals with complex interference from high-fidelity physical environment modeling, integrating and testing advanced underwater acoustic processing algorithms, and ultimately conducting quantitative, closed-loop evaluation from multiple application perspectives. This leads to significant uncertainties in the design, algorithm development, and performance prediction of sonar array systems, prolonging the development cycle and increasing the cost and risk of experimental testing. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide an array signal processing simulation and evaluation system for underwater sonar systems.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A simulation and evaluation system for array signal processing in underwater sonar systems, comprising: The underwater acoustic channel simulation module is used to generate an underwater channel model that includes acoustic ray bending and multipath effects based on the input marine environmental parameters, and to calculate the channel response of each array element based on the underwater channel model. The signal synthesis module is connected to the underwater acoustic channel simulation module and is used to synthesize simulated array multi-channel received signals based on the set sonar array parameters, target signal parameters and channel response. The algorithm processing module, connected to the signal synthesis module, integrates an underwater acoustic signal processing algorithm library to process simulated array multi-channel received signals and output beamforming results or spatial spectrum estimation results. The performance evaluation module, connected to the algorithm processing module, is used to visualize the processing results and calculate performance evaluation metrics, including array gain and detection probability.

[0010] As a further preferred embodiment of the present invention, the underwater acoustic channel simulation module generates an underwater channel model and calculates the channel response of each array element, specifically including the following steps: Receive input marine environmental parameters, including sound velocity profile data, sea depth data, seabed topography data, and seabed acoustic parameters; Based on sound velocity profile data and ocean depth data, the ray tracing algorithm is used to calculate the ray trajectory of all propagation paths of sound waves from the sound source point to each element of the underwater sonar array. Among them, ocean depth data is used together with the sound source depth and array depth to determine the vertical boundary of sound ray propagation and to determine whether the sound ray is reflected by the sea surface or the seabed. For each sound ray trajectory, the propagation delay is calculated based on its propagation path length, and its amplitude attenuation is calculated based on the propagation loss model; For the reflected sound ray trajectory, the additional amplitude attenuation and phase change brought about by the reflection path are calculated by combining the sea surface reflection model or the seabed reflection coefficient corresponding to the seabed acoustic parameters. By combining the time delay, amplitude, and phase information of all sound ray trajectories from the sound source point to the same array element, the channel impulse response corresponding to the array element is constructed, which serves as the channel response of the channel in the underwater channel model. The above process is repeated for each element in the array to generate an underwater channel model, which is used to characterize the spatial multipath propagation characteristics from the sound source to all elements of the array.

[0011] As a further preferred embodiment of the present invention, the signal synthesis module synthesizes a simulated array multi-channel received signal based on the set sonar array parameters, target signal parameters, and the channel response, specifically including the following steps: The system receives input sonar array parameters, target signal parameters, and the channel response of the underwater acoustic channel simulation module. The sonar array parameters include the number of array elements, the three-dimensional spatial position of each element, and its directivity. The target signal parameters include the signal type, center frequency, bandwidth, transmitted waveform, and the target's dynamic position and motion state. Generate a baseband transmission signal based on the target signal parameters; Based on the target's dynamic position and sonar array parameters, calculate the geometric propagation delay of the transmitted signal from the target position to each array element; For each array element, the baseband transmitted signal is time-shifted according to the corresponding geometric propagation delay, and convolution is performed using the channel response corresponding to that array element to simulate the distortion and multipath superposition effect of the signal after propagation through a multipath underwater acoustic channel. Background noise and reverberation interference are added to the channel signals after channel convolution to generate the final time-domain received signals of each array element. The time-domain received signals of all array elements are output as simulated array multi-channel received signals, which are used as input to the algorithm processing module.

[0012] As a further preferred embodiment of the present invention, the algorithm processing module processes the simulated array multi-channel received signal and outputs beamforming results or spatial spectrum estimation results, specifically including the following steps: Receives analog array multichannel received signals from the signal synthesis module; Select at least one signal processing algorithm from the integrated underwater acoustic signal processing algorithm library; the underwater acoustic signal processing algorithm library includes conventional beamforming algorithms, adaptive beamforming algorithms, and spatial spectrum estimation algorithms based on subspace decomposition. The selected signal processing algorithm is used to process the multi-channel received signal of the array; when the beamforming algorithm is selected, beams pointing to different azimuths or elevation angles are calculated and formed, and the time domain or frequency domain results after beamforming and their corresponding beam pattern are output; when the spatial spectrum estimation algorithm is selected, the spatial power spectrum distribution of the signal is calculated and the spatial spectrum estimation results containing the azimuth of potential targets are output.

[0013] As a further preferred embodiment of the present invention, the performance evaluation module visualizes the processing results and calculates performance evaluation indicators, specifically including the following steps: Receive beamforming results or spatial spectrum estimation results from the algorithm processing module; Based on the received processing results, generate and display at least one of the following visualization charts: beam pattern, spatial spectrum, and azimuth-time history chart; Based on the processing results, the preset target parameters of the simulation input, and the noise and reverberation levels added by the signal synthesis module, performance evaluation indicators are calculated. The performance evaluation indicators include: array processing gain, output signal-to-noise ratio improvement factor, detection probability under a given false alarm probability condition, and target azimuth and distance estimation accuracy. Generate and output an evaluation report that includes performance evaluation metrics data and corresponding visualization charts.

[0014] As a further preferred embodiment of the present invention, the underwater acoustic channel simulation module is also used to generate a time-varying and space-varying coherent interference field, specifically including: Based on sound velocity profile data and ocean depth data, the normal modes of sound waves of a specific frequency in vertical space and the propagation constants of each mode are calculated. Based on seabed topographic data and seabed acoustic parameters, the coupling and attenuation of each normal mode wave along the propagation path are calculated. By utilizing the calculated normal mode function and its propagation characteristics, the interference source channel response from the location of the interference source to each element of the array is synthesized; this interference source channel response is used to simulate strong coherent interference existing in a specific direction underwater, including hostile sound sources or fixed noise sources. The channel response of the interference source and the channel response of the corresponding target sound source are superimposed and output to the signal synthesis module. When the signal synthesis module synthesizes the received signal, it convolves and superimposes the simulated interference source signal through its corresponding physical channel response to generate a coherent interference component with real spatial correlation and multipath structure in the array received signal.

[0015] As a further preferred embodiment of the present invention, it also includes an array platform motion simulation and motion compensation processing module, which specifically performs the following steps: Receive preset sonar platform motion parameters, including the platform's motion trajectory, speed, and attitude changes; Based on the motion parameters of the sonar platform and the parameters of the sonar array, calculate the sequence of position changes of each array element relative to the initial geometry within the signal reception time window; In the signal synthesis module, the geometric delay of signal propagation and the phase center of array elements are dynamically corrected according to the position change sequence; In the algorithm processing module, a motion-compensated beamforming algorithm based on prior information of motion parameters is provided. When calculating the beamforming weights, the motion-compensated beamforming algorithm compensates for the array manifold changes caused by the platform motion.

[0016] The beneficial effects of this invention are as follows: This invention achieves high-fidelity, physically reliable underwater acoustic channel simulation, fundamentally improving the realism of the simulation environment and the credibility of the evaluation results. Through a specifically defined ray tracing algorithm based on sound velocity profiles and ocean depth data, this invention can accurately calculate sound ray bending and reflection paths, constructing a channel impulse response that includes a realistic multipath structure. This ensures that the generated underwater channel model is no longer based on ideal assumptions but strictly depends on the input ocean physical parameters, thus providing a simulation foundation that closely approximates the real physical environment for subsequent signal synthesis and algorithm processing. This directly overcomes the fundamental problem pointed out in the background art: overly simplified underwater channel modeling leads to distortion of the simulation environment.

[0017] This invention provides the capability to synthesize complex interference based on a physical model, simulating coherent interference with realistic spatial correlation, greatly enriching adversarial testing scenarios. The underwater acoustic channel simulation module of this invention can synthesize the channel response of a coherent interference source from a specific direction using normal mode theory. This response, along with the target channel response, is fed into the signal synthesis module, ensuring that the final array received signal includes not only environmental noise and reverberation but also hostile interference signals propagating through the same complex underwater acoustic channel and possessing correct space-time-frequency correlation characteristics. This addresses the shortcomings of incomplete interference simulation and lack of physical-level modeling in previous technologies, enabling the system to evaluate the core anti-jamming performance of sonar and processing algorithms in complex electronic warfare environments—something general-purpose simulation systems cannot achieve.

[0018] This invention constructs an automated, closed-loop workflow from simulation and processing to evaluation, and provides multi-dimensional quantitative performance evaluation indicators, making the evaluation conclusions more instructive. Through the organic connection of four core modules (channel simulation, signal synthesis, algorithm processing, and performance evaluation), this invention forms a complete simulation and evaluation chain. In particular, the performance evaluation module not only provides visual demonstrations but also calculates multi-dimensional indicators including array processing gain, detection probability, and azimuth / range estimation accuracy. These indicators are closely integrated with the application requirements of sonar (such as detection capability with low false alarm rates and positioning accuracy), elevating the evaluation from simple algorithm demonstrations to the level of quantitative analysis of system-level performance. This solves the problems of disconnect between simulation and evaluation, and the single evaluation dimension in the background technology.

[0019] This system fully considers the dynamic deployment scenarios of actual sonar systems, integrating platform motion simulation and compensation mechanisms to enhance the practicality and coverage of the simulation system. Based on the introduced array platform motion simulation and motion compensation processing module, the system can simulate array geometric deformation and signal Doppler effects caused by the motion of the sonar carrier (such as ships or towed bodies), and provides corresponding motion compensation beamforming capabilities at the algorithm layer. This extends the simulation evaluation scenario from static to dynamic, effectively verifying the algorithm's performance retention capability on actual moving platforms and the effectiveness of the motion compensation algorithm, overcoming the challenge of insufficiently considering the dynamic nature of actual deployment in previous technologies.

[0020] In summary, this invention constructs a dedicated simulation and evaluation system that is end-to-end, high-fidelity, scenario-complete, and deeply evaluated through a series of mutually supporting and progressively advancing technical solutions. It not only significantly improves the efficiency and accuracy of array signal processing algorithm development and verification, but also provides strong digital support for the forward design, tactical performance prediction, and operational use research of underwater sonar systems, effectively reducing engineering development risks and experimental costs. Attached Figure Description

[0021] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a schematic diagram of the structure of an array signal processing simulation and evaluation system for underwater sonar systems according to the present invention; Figure 2 This is a schematic diagram illustrating the execution steps of the underwater acoustic channel simulation module of the present invention; Figure 3 This is a schematic diagram illustrating the execution steps of the signal synthesis module of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] like Figure 1-3 As shown in the figure, an embodiment of the present invention provides a simulation and evaluation system for array signal processing of underwater sonar systems, which mainly includes an underwater acoustic channel simulation module, a signal synthesis module, an algorithm processing module, and a performance evaluation module. These modules are connected sequentially to form a complete closed loop from environment modeling, signal generation, algorithm processing to performance evaluation.

[0024] Detailed implementation of the underwater acoustic channel simulation module: The underwater acoustic channel simulation module is the foundation of this system. Its core task is to generate a high-fidelity underwater channel model based on the input marine environmental parameters and calculate the channel response of each array element. The specific implementation steps are as follows: Step S201: Receive marine environmental parameters: Input parameters include: sound velocity profile data (Speed ​​of sound with depth) Changes), ocean depth data Seabed topographic data And seabed acoustic parameters (such as seabed density, sound velocity, attenuation coefficient, etc.).

[0025] By receiving multi-dimensional, high-precision measured or simulated environmental parameters, a high degree of matching between the simulated environment and the real sea area is ensured, providing data assurance for building a physically reliable channel model.

[0026] Step S202: Calculate the sound ray trajectory based on the ray tracing algorithm: Based on sound speed profile He Haishen The ray acoustic theory (Snell's law) was used to calculate the sound from the source point. to each array element ( , The trajectories of sound rays along all possible propagation paths (to the number of array elements). Each sound ray... The propagation path consists of a set of depth-horizontal distance points The trajectory is described as satisfying the differential equation: Numerical solution to the equation yields the acoustic ray trajectory. During this process, ocean depth... With the depth of the sound source and array depth Together, they determined the vertical boundary of sound ray propagation. The algorithm determines whether the sound ray is perpendicular to the sea surface (…). ) or seabed ( The points intersect, and the location of the intersection is recorded.

[0027] By employing a ray tracing algorithm, the bending propagation of sound waves in non-uniform water bodies can be accurately simulated, overcoming the limitations of the traditional plane wave assumption and making the calculated wave arrival direction more consistent with physical reality.

[0028] Step S203: Calculate the propagation delay and amplitude attenuation of each sound ray trajectory: For the The trajectory of the sound ray, the length of its propagation path is Propagation delay for: Amplitude attenuation By extended loss and absorption loss composition: in, It is a frequency-dependent absorption coefficient.

[0029] By accurately calculating the delay and loss of each path, a foundation was laid for the subsequent construction of a multipath channel response with accurate time-domain and amplitude characteristics.

[0030] Step S204: Calculate the additional attenuation and phase change of the reflection path: For sound rays that are reflected from the sea surface or seabed, the angle of incidence at the reflection point is used as the determining factor. Calculate the reflection coefficient based on the characteristics of the reflecting surface. Sea surface reflection is typically modeled as perfect reflection with a small random phase shift. Seabed reflectivity The Rayleigh reflection coefficient formula or empirical model is used to calculate the amplitude of the reflected signal based on the acoustic parameters of the substrate and the angle of incidence. Phase introduces changes .

[0031] The refined reflection modeling enables the synthesized multipath signal to include amplitude and phase variations determined by boundary physical properties, significantly improving the simulation realism of multipath effects.

[0032] Step S205: Construct the channel impulse response of a single array element: All from the sound source To the same array of The contributions of each sound ray (including direct and reflected paths) are superimposed to construct the channel impulse response of the array element. : in, It is the Dirac function. This response characterizes the multipath propagation effect experienced by the signal from the sound source to the array element.

[0033] By superimposing all effective acoustic paths, an impulse response that accurately describes the complex underwater multipath channel is formed, providing a precise channel convolution kernel for subsequent signal synthesis.

[0034] Step S206: Generate the underwater channel model for the entire array: For each element in the array ( Repeat steps S202 to S205 to obtain a set of channel impulse responses. This set of responses constitutes the underwater channel model for this simulation. It fully characterizes the spatial multipath propagation characteristics from the sound source to the entire array.

[0035] A spatial channel model tightly coupled with array geometry was generated, enabling the simulation to reflect the differentiated multipath structure experienced by the signal on different array elements. This is a prerequisite for achieving high-precision spatial processing simulation.

[0036] Specific implementation of the signal synthesis module: The signal synthesis module uses an underwater acoustic channel model, set array and target parameters to synthesize a simulated multi-channel array received signal. The specific implementation steps are as follows: Step S301: Receive input parameters: Received sonar array parameters (number of elements) 3D positions of each array element directional ), Target signal parameters (signal type, center frequency) ,bandwidth Transmitted waveform The dynamic position of the target (and the channel response output from the underwater acoustic channel simulation module) .

[0037] It integrates system configuration, target scene, and physical channel information, thus preparing the data for generating synthetic signals that closely resemble reality.

[0038] Step S302: Generate baseband transmit signal: Generate baseband transmit signal based on signal type and waveform parameters. For example, for a linear frequency modulation (LFM) signal: in, The pulse width. For frequency modulation slope, This is a rectangular window function.

[0039] It can flexibly generate various common and complex underwater acoustic signals to meet the simulation needs of different modes such as active sonar and passive listening.

[0040] Step S303: Calculate the geometric propagation delay: According to the target at time dynamic position With the position of the array element Calculate the straight-line distance The corresponding geometric propagation delay is ,in For reference speed of sound.

[0041] The relative motion between the target and the array is introduced, so that the synthesized signal contains time-varying time delay and Doppler information, supporting dynamic scene simulation.

[0042] Step S304: Perform channel convolution and multipath superposition: For each array element After shifting the baseband transmitted signal according to the geometric time delay, its channel impulse response is compared with... Convolution is performed to simulate the distortion of a signal after propagation through a multipath channel: in This indicates a convolution operation. This step generates signal components that include multipath superposition effects.

[0043] By combining the theoretical transmitted signal with a high-fidelity channel model through convolution operations, a signal with a real multipath structure and propagation distortion is generated, which is something that traditional additive noise models cannot achieve.

[0044] Step S305: Add background noise and reverberation interference: Background noise: Generate a spatially correlated environmental noise field. First, generate the power spectral density based on a deep-sea or shallow-sea noise spectral model. Then, the independent noise of each array element is obtained through a colored noise generation method. Furthermore, based on the spatial coherence model of the noise field... (For example, for an isotropic noise field,) ,in (element spacing) is used to spatially filter independent noise, generating noise with correct spatial correlation. .

[0045] Reverberation interference: For active sonar modes, a unit scattering model is used to synthesize reverberation. The seabed / surface is divided into multiple scattering units, each unit... The scattered signal is: in, The scattering intensity is related to the incident angle, frequency, and substrate. This is for two-way propagation delay; The scattering phase is random. The signals from all scattering elements are coherently superimposed to obtain the array elements. reverberation .

[0046] Non-white noise and strongly correlated reverberation interference that conform to the physical characteristics of underwater acoustics were generated, which greatly improved the realism and challenge of the simulation signal and made it possible to evaluate the performance of the algorithm in harsh environments.

[0047] Step S306: Generate and output the final multi-channel received signal: The target multipath signal, background noise, and reverberation interference are superimposed to form the final time-domain received signal for each array element: Output all Signal of each channel , which serves as the input to the algorithm processing module.

[0048] The output contains a complete synthetic signal including the target, ambient noise, reverberation, and even coherent interference, providing a highly integrated and physically meaningful test input for subsequent algorithm processing modules.

[0049] Detailed implementation of the algorithm processing module The algorithm processing module integrates multiple underwater acoustic signal processing algorithms to process the synthesized received signal and output beamforming or spatial spectrum estimation results. The specific implementation steps are as follows: Step S401: Receive multi-channel received signals: Receive from the signal synthesis module Channel time domain data It is usually discretized into , , This represents the number of sampling points. It provides a standardized data interface for algorithm processing.

[0050] Step S402: Select and apply a signal processing algorithm: Select at least one algorithm from the integrated algorithm library for processing. The algorithm library mainly includes: Conventional beamforming (CBF): The weight vector is ,in Is the array in the direction The guide vector on the beam. The beam output is... Scan different The beam pattern is obtained.

[0051] Adaptive beamforming (e.g., MVDR): The weight vector is ,in This is an estimate of the covariance matrix of the received data. The algorithm effectively suppresses interference.

[0052] Subspace-type spatial spectrum estimation (e.g., MUSIC): for Perform feature decomposition and utilize the noise subspace Computational spatial spectrum .

[0053] A series of processing algorithms are provided, which users can flexibly choose according to the evaluation target, facilitating horizontal comparative analysis.

[0054] Step S403: Output the processing result: If a beamforming algorithm is selected, the output will be the time-domain / frequency-domain result after beamforming. and beam pattern .

[0055] If the spatial spectrum estimation algorithm is selected, the output spatial spectrum will be... Its peak position indicates the target's location.

[0056] It outputs intuitive and quantitative processing results, providing a direct data foundation for performance evaluation.

[0057] Detailed implementation of the performance evaluation module The performance evaluation module visualizes the algorithm processing results and calculates multiple performance metrics. The specific implementation steps are as follows: Step S501: Receive the algorithm processing result: Receive beam pattern from algorithm processing module or spatial spectrum Wait for the results data.

[0058] Step S502: Visualization: Generate and display at least one of the following charts: Beam pattern: in azimuth angle The x-axis represents the normalized beam response. The vertical axis is denoted by .

[0059] Spatial Spectrum: by azimuth The x-axis represents the spatial spectral values. The vertical axis is denoted by .

[0060] Azimuth-Time History (BTR): With time on the horizontal axis and azimuth on the vertical axis, the energy of each azimuth-time unit is represented by color intensity.

[0061] Transforming abstract data into intuitive graphics makes it easier for researchers to quickly grasp the system's performance overview and target status.

[0062] Step S503: Calculate performance evaluation metrics: Based on the processing results and known simulation true values ​​(target location, signal energy, noise level, etc.), the following core indicators are calculated: 1. Array processing gain (AG): The input and output signal-to-noise ratios are calculated using energy.

[0063] 2. Detection probability (Pd) and false alarm probability (Pfa): Statistical results obtained through Monte Carlo simulation under a given detection threshold. These are typically plotted... Follow The changing curve (ROC curve).

[0064] 3. Azimuth estimation accuracy: Calculate the estimated azimuth. with actual location Root mean square error (RMSE): ,in The number of Monte Carlo experiments.

[0065] 4. Distance estimation accuracy: In active mode, the RMSE of distance estimation can be calculated similarly.

[0066] It provides a multi-level, quantifiable, and objective evaluation standard, enabling the performance of different algorithms or system configurations to be accurately compared and measured.

[0067] Step S504: Generate and output the evaluation report: Automatically summarizes all visualization charts and performance metrics data, generating a structured evaluation report (such as PDF or HTML format). The report includes a simulation configuration summary, key result charts, performance metric tables, and a brief conclusion analysis.

[0068] The evaluation process has been automated and standardized, which facilitates the archiving, sharing and comparative study of results, and significantly improves R&D efficiency.

[0069] Specific implementation details of coherent interference simulation: The specific implementation steps of the underwater acoustic channel simulation module in generating time-varying and space-varying coherent interference fields are as follows: Step S601: Set the interference source parameters and calculate the normal mode: Configure parameters for coherent interference sources, including their geographical location. ,depth Spectral characteristics (center frequency) Based on the current simulation of the sound velocity profile. With the depth of the sea Solve the following depth-independent normal wave equations (or use a numerical algorithm such as Kraken): in, It is angular frequency. It is the first The horizontal wavenumber of the first normal mode is obtained. A set of normal mode function functions is obtained by solving. and its corresponding horizontal wavenumber and attenuation coefficient .

[0070] Using normal mode theory to globally model the sound field in low-frequency or complex waveguide environments can more accurately describe the interference and attenuation structure of sound waves at long distances or in the presence of strong waveguide effects. It is particularly suitable for simulating long-range coherent interference in environments such as deep-sea acoustic channels.

[0071] Step S602: Calculate the propagation and coupling of each mode: Based on seabed topographic data and seabed acoustic parameters, the coupling coefficient and additional attenuation of each normal mode wave along the propagation path from the interference source to the receiving array are calculated. This involves calculating the mode energy conversion and loss in a horizontally non-uniform waveguide.

[0072] Step S603: Synthesize the channel response from the interference source to the array: Using the calculated normal mode function And its propagation characteristics, synthesized from the interference source (depth ) to array number Individual elements (depth) Frequency domain channel response For distance Given the source and receiver points, under point source excitation, the sound field can be expressed as the sum of the various modes: By summing all relevant modes, the channel transfer function, which includes waveguide interference and attenuation effects, is obtained.

[0073] Step S604: Generate the time-domain interference source channel impulse response: For the above frequency domain response Performing an inverse Fourier transform yields the time-domain channel impulse response. This response reflects the multimode superposition, time delay spread, and dispersion characteristics of coherent interference signals after propagation through complex underwater acoustic waveguides.

[0074] Step S605: Integrate coherent interference into the signal synthesis module: 1. In the signal synthesis module, a baseband interference signal is generated based on the interference source signal parameters. .

[0075] 2. For each array element To interfere with the signal Its corresponding channel impulse response Perform convolution: The signal This refers to coherent interference components that have been shaped by the physical channel and possess real space-time-frequency correlation characteristics.

[0076] 3. During the final synthesis of the received signal (corresponding to the original step S306), the coherent interference component is superimposed with the target multipath signal, ambient noise, reverberation, etc. Through the aforementioned physical-level modeling based on normal mode theory, the system can synthesize highly realistic coherent interference. This interference is not only directional but also carries a unique spatial interference structure and frequency dependence determined by the physical characteristics of ocean waveguides. This allows the simulation system to realistically simulate scenarios such as "long-range enemy sonar signals propagating through deep-sea acoustic channels" or "complex coherent noise received by fixed monitoring systems," thereby enabling extremely rigorous and reliable evaluation of the spatial interference suppression capability and waveguide environment adaptability of the array signal processing algorithm.

[0077] Specific implementation details of platform motion simulation and compensation: The specific implementation of the array platform motion simulation and motion compensation processing module is as follows: 1. Motion parameter input: Receive platform motion trajectory ,speed And attitude (roll, pitch, roll) data.

[0078] 2. Array element position sequence calculation: based on the platform's initial configuration (The position of the array element in the platform coordinate system) and the platform motion parameters are used to calculate the position of the array element at each moment. The actual position of the array element in the geodetic coordinate system ,in It is a rotation matrix.

[0079] 3. Signal synthesis correction: In step S303 of the signal synthesis module, time-varying array element positions are used. Computational geometric delay .

[0080] 4. Motion-Compensated Beamforming: The algorithm processing module provides a motion compensation algorithm. For example, in the MVDR algorithm, the steering vector... It needs to be based on the real-time array element position Calculate. The compensated weight vector is: .

[0081] The problem of time-varying array manifold caused by the motion platform was solved, enabling the simulation system to evaluate the performance of the sonar system under real navigation conditions and verify the effectiveness of the motion compensation algorithm.

[0082] The working principle and process of this invention are as follows: First, the user configures the marine environment, sonar array, target, and interference parameters. The underwater acoustic channel simulation module calculates a high-fidelity channel model based on physical laws. The signal synthesis module uses this model, combined with target and interference signals, to generate a multi-channel received signal including noise, reverberation, and coherent interference. The algorithm processing module applies the selected algorithm to process the signal. Finally, the performance evaluation module visualizes and quantitatively evaluates the processing results, outputting a comprehensive and objective performance report. The entire process forms a closed loop, providing a powerful digital simulation tool for the design, algorithm optimization, and performance prediction of underwater sonar array systems.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A simulation and evaluation system for array signal processing in underwater sonar systems, characterized in that, include: The underwater acoustic channel simulation module is used to generate an underwater channel model that includes acoustic ray bending and multipath effects based on the input marine environmental parameters, and to calculate the channel response of each array element based on the underwater channel model. The signal synthesis module is connected to the underwater acoustic channel simulation module and is used to synthesize simulated array multi-channel received signals based on the set sonar array parameters, target signal parameters and channel response. The algorithm processing module, connected to the signal synthesis module, integrates an underwater acoustic signal processing algorithm library to process simulated array multi-channel received signals and output beamforming results or spatial spectrum estimation results. The performance evaluation module, connected to the algorithm processing module, is used to visualize the processing results and calculate performance evaluation metrics, including array gain and detection probability.

2. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 1, characterized in that: The underwater acoustic channel simulation module generates an underwater channel model and calculates the channel response of each array element, specifically including the following steps: Receive input marine environmental parameters, including sound velocity profile data, sea depth data, seabed topography data, and seabed acoustic parameters; Based on sound velocity profile data and ocean depth data, the ray tracing algorithm is used to calculate the ray trajectory of all propagation paths of sound waves from the sound source point to each element of the underwater sonar array. Among them, ocean depth data is used together with the sound source depth and array depth to determine the vertical boundary of sound ray propagation and to determine whether the sound ray is reflected by the sea surface or the seabed. For each sound ray trajectory, the propagation delay is calculated based on its propagation path length, and its amplitude attenuation is calculated based on the propagation loss model; For the reflected sound ray trajectory, the additional amplitude attenuation and phase change brought about by the reflection path are calculated by combining the sea surface reflection model or the seabed reflection coefficient corresponding to the seabed acoustic parameters. By combining the time delay, amplitude, and phase information of all sound ray trajectories from the sound source point to the same array element, the channel impulse response corresponding to the array element is constructed, which serves as the channel response of the channel in the underwater channel model. The above process is repeated for each element in the array to generate an underwater channel model, which is used to characterize the spatial multipath propagation characteristics from the sound source to all elements of the array.

3. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 1, characterized in that: The signal synthesis module synthesizes a simulated multi-channel received signal based on the set sonar array parameters, target signal parameters, and the channel response. Specifically, this includes the following steps: The system receives input sonar array parameters, target signal parameters, and the channel response of the underwater acoustic channel simulation module. The sonar array parameters include the number of array elements, the three-dimensional spatial position of each element, and its directivity. The target signal parameters include the signal type, center frequency, bandwidth, transmitted waveform, and the target's dynamic position and motion state. Generate a baseband transmission signal based on the target signal parameters; Based on the target's dynamic position and sonar array parameters, calculate the geometric propagation delay of the transmitted signal from the target position to each array element; For each array element, the baseband transmitted signal is time-shifted according to the corresponding geometric propagation delay, and convolution is performed using the channel response corresponding to that array element to simulate the distortion and multipath superposition effect of the signal after propagation through a multipath underwater acoustic channel. Background noise and reverberation interference are added to the channel signals after channel convolution to generate the final time-domain received signals of each array element. The time-domain received signals of all array elements are output as simulated array multi-channel received signals, which are used as input to the algorithm processing module.

4. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 1, characterized in that: The algorithm processing module processes the simulated multi-channel array received signal and outputs beamforming results or spatial spectrum estimation results, specifically including the following steps: Receives analog array multichannel received signals from the signal synthesis module; Select at least one signal processing algorithm from the integrated underwater acoustic signal processing algorithm library; the underwater acoustic signal processing algorithm library includes conventional beamforming algorithms, adaptive beamforming algorithms, and spatial spectrum estimation algorithms based on subspace decomposition. The selected signal processing algorithm is used to process the multi-channel received signal of the array; when the beamforming algorithm is selected, beams pointing to different azimuths or elevation angles are calculated and formed, and the time domain or frequency domain results after beamforming and their corresponding beam pattern are output; when the spatial spectrum estimation algorithm is selected, the spatial power spectrum distribution of the signal is calculated and the spatial spectrum estimation results containing the azimuth of potential targets are output.

5. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 3, characterized in that: The performance evaluation module visualizes the processing results and calculates performance evaluation metrics, specifically including the following steps: Receive beamforming results or spatial spectrum estimation results from the algorithm processing module; Based on the received processing results, generate and display at least one of the following visualization charts: beam pattern, spatial spectrum, and azimuth-time history chart; Based on the processing results, the preset target parameters of the simulation input, and the noise and reverberation levels added by the signal synthesis module, performance evaluation indicators are calculated. The performance evaluation indicators include: array processing gain, output signal-to-noise ratio improvement factor, detection probability under a given false alarm probability condition, and target azimuth and distance estimation accuracy. Generate and output an evaluation report that includes performance evaluation metrics data and corresponding visualization charts.

6. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 2, characterized in that: The underwater acoustic channel simulation module is also used to generate time-varying and space-varying coherent interference fields, specifically including: Based on sound velocity profile data and ocean depth data, the normal modes of sound waves of a specific frequency in vertical space and the propagation constants of each mode are calculated. Based on seabed topographic data and seabed acoustic parameters, the coupling and attenuation of each normal mode wave along the propagation path are calculated. By utilizing the calculated normal mode function and its propagation characteristics, the interference source channel response from the location of the interference source to each element of the array is synthesized; this interference source channel response is used to simulate strong coherent interference existing in a specific direction underwater, including hostile sound sources or fixed noise sources. The channel response of the interference source and the channel response of the corresponding target sound source are superimposed and output to the signal synthesis module. When the signal synthesis module synthesizes the received signal, it convolves and superimposes the simulated interference source signal through its corresponding physical channel response to generate a coherent interference component with real spatial correlation and multipath structure in the array received signal.

7. The array signal processing simulation and evaluation system for underwater sonar systems according to claim 1, characterized in that: It also includes an array platform motion simulation and motion compensation processing module, which specifically performs the following steps: Receive preset sonar platform motion parameters, including the platform's motion trajectory, speed, and attitude changes; Based on the motion parameters of the sonar platform and the parameters of the sonar array, calculate the sequence of position changes of each array element relative to the initial geometry within the signal reception time window; In the signal synthesis module, the geometric delay of signal propagation and the phase center of array elements are dynamically corrected according to the position change sequence; In the algorithm processing module, a motion-compensated beamforming algorithm based on prior information of motion parameters is provided. When calculating the beamforming weights, the motion-compensated beamforming algorithm compensates for the array manifold changes caused by the platform motion.

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