1898 results about "Hydroacoustics" patented technology

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention discloses a coastal sound tomography data assimilation method based on stream function fitting. The method comprises the following steps: arranging a coastal sound tomography system, obtaining underwater sound data containing observation domain hydrological information, carrying out correlation peak extraction, and calculating to obtain a sound ray mutual return transmission time difference; scanning topographic information of an observation domain and temperature and profile data between stations, inputting the topographic information and the temperature and profile data into a sound velocity model for sound ray simulation, determining an actual propagation path of sound rays, and inverting path average flow; constructing a three-dimensional unstructured grid of the observation domain, and determining a projection relation between an actual propagation path of sound rays and the grid; establishing a state transition matrix by taking a sound ray path average flow as an observation vector of extended Kalman filtering and taking a coefficient matrix of a flow function least square fitting ocean numerical pattern calculation result as a state vector; and assimilating and updating by using an extended Kalman filtering algorithm, and obtaining an optimized flow field. According to the invention, the observation precision, the resolution and the spatial range of the coastal flow field can be obviously improved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

The invention relates to the related field of underwater acoustic signal processing and noise recognition, in particular to an underwater acoustic radiation noise recognition method, and provides an underwater acoustic radiation noise real-time recognition system based on combination of a convolutional neural network (CNN) and a long short-term memory network (LSTM). The system can perform efficient data preprocessing, feature extraction and optimization processing after collecting underwater acoustic signals in real time, and performs rapid identification of noise types through the trained mixed deep learning model. Specifically, the CNN can extract local features from a spectrogram, the LSTM is good at capturing time sequence information, complex underwater acoustic signals can be better understood and distinguished through combination of the CNN and the LSTM, and the recognition precision is remarkably improved. In addition, the system is further provided with an automatic updating mechanism, the model can be retrained regularly according to newly-collected data, it is ensured that the model is always in the optimal state, and the system adapts to the constantly-changing underwater environment.

The invention discloses an intelligent underwater sound signal processing system and method based on multi-sensor data fusion, and belongs to the field of underwater signal processing. A plurality of sensors are used for collecting underwater sound signals, linear acceleration, angular velocity and other information of an underwater target and outputting continuous analog signals; then the differential signals are transmitted to a signal conditioning module, gain is dynamically adjusted according to target intensity, amplified differential signals are output to an analog-to-digital conversion module, high-precision digitalization is carried out through a 24-bit delta-sigma ADC, digital signals are output to a main control unit, and the main control unit carries out multi-sensor data fusion and target orientation calculation; and finally, the main control unit uploads the underwater acoustic signal and the equipment attitude information data to an upper computer through an RS485 bus for man-machine interaction. The scheme is based on multi-sensor data fusion, can realize high-precision target detection and positioning in a dynamic underwater environment, and has wide popularization and application values.

The invention relates to the technical field of submarine engineering and information processing, in particular to a submarine cable positioning method and system, and the method comprises the steps: synchronously collecting magnetic field, structured light and underwater acoustic signal data, and fusing inertial navigation information to construct a unified space-time reference system; precise registration of heterogeneous data is realized by adopting sliding time window screening, multi-modal noise reduction and rigid body transformation; constructing a graph structure model for expressing cable space-signal features based on the fused data, and reasoning cable path topology by using a graph convolutional network; reconstructing a cable center line through three-dimensional visualization, embedding the cable center line into submarine topography, and realizing risk area dynamic clustering by combining signal integrity score and burial depth estimation; historical time sequence data are fused, a long short-term memory network is adopted to predict cable drift, detectable attenuation and burial depth change trends, risk levels are divided based on prediction results, and spatial distribution early warning and disposal suggestions are output. According to the invention, the cable positioning precision and the risk assessment intelligent level in a complex seabed environment are improved.

The invention discloses a bimodal neural network and spectral subtraction combined underwater acoustic signal noise suppression method. Comprising the following steps: firstly, constructing a simulation underwater sound data set containing self-noise; thirdly, constructing a bimodal neural network, then training the bimodal neural network until training is completed, and obtaining a platform self-noise estimation model; inputting a to-be-processed noise-containing underwater acoustic signal into the platform self-noise estimation model, and outputting estimation platform self-noise by the model; and finally, based on the self noise of the estimation platform, noise reduction processing is carried out on the noise-containing underwater acoustic signal to be processed by using the improved spectral subtraction method, and then a noise-reduced underwater acoustic signal is obtained. The method combines an advanced deep learning method and a classical spectral subtraction algorithm, has the capabilities of autonomous learning and efficient noise reduction, remarkably improves the signal-to-noise ratio of the underwater acoustic signal, is suitable for noise reduction of the underwater acoustic signal in a non-stationary and complex marine environment, remarkably improves the low-frequency noise suppression capability and the target signal fidelity, and has a good application prospect. The method is suitable for underwater acoustic target detection, underwater acoustic communication and other scenes.

The invention discloses an automatic leveling method for intelligently controlling an underwater leveling machine. The method comprises the following steps: acquiring real-time attitude data and working surface flatness requirements of a plurality of execution mechanisms of the underwater leveling machine, and constructing state and operation action information characteristics; an execution mechanism is mapped into an agent with a body, information is shared through an underwater acoustic communication network, and each agent selects and executes a leveling action based on self and neighborhood interaction data; after execution, obtaining a reward value, storing the reward value in an experience pool, sampling and updating the state-action function, and performing iterative training to obtain a multi-body agent reinforcement learning optimization model; continuously monitoring the state of a mechanism when the model runs, dynamically updating the features of an affected area when a fault or hydrodynamic abnormality is identified, and recalculating a leveling strategy to obtain a dynamic re-planning scheme; the scheme is combined with model output to generate a real-time leveling instruction sequence, and the real-time leveling instruction sequence is decomposed and then issued to each execution mechanism, so that self-adaptive accurate leveling control of the underwater leveling machine is realized, and the working efficiency and reliability of a complex underwater environment are effectively improved.

The invention discloses a deep-sea self-expanding horizontal array acoustic subsurface buoy, which relates to the field of deep-sea underwater acoustic detection and comprises an expanding platform arranged at the top of the acoustic subsurface buoy, a subsurface buoy main body positioned below the expanding platform and an anchoring structure connected below the subsurface buoy main body, the subsurface buoy body comprises a buoyancy platform arranged below the unfolding platform and an equipment platform arranged below the buoyancy platform. The problem that a hydrophone linear array composed of a plurality of large-scale acoustic linear arrays is folded and automatically unfolded into a cross horizontal array is solved, and integration of a deep-sea acoustic emission system, an acoustic receiving system, an underwater acoustic communication system and an equipment supply system is achieved. The whole subsurface buoy can be quickly and conveniently released and recycled, and high-reliability anchor mooring is achieved.

Disclosed in the present invention is an underwater acoustic target recognition method based on effective receptive field regulation. A proposed AEU-Net model has a plurality of resolution branches, with each resolution branch having independent convolution kernels, which adaptively adjust their sizes during training while aligning with their respective resolutions. The AEU-Net model comprises an ERF-Server, which can regulate an effective receptive field during forward propagation, and has two operations of increasing or decreasing the effective receptive field of a feature map of any designated module. In the present invention, acoustic physical information of an underwater acoustic target sample can be captured in a plurality of interaction dimensions, and feature fusion of information from effective receptive fields across a plurality of scales is performed, so as to adapt to sonar image targets of different sizes, thereby improving the accuracy and recognition speed in the recognition of an underwater acoustic target.

The invention provides an underwater acoustic target recognition method based on autonomous task perception, and belongs to the field of underwater acoustic signal processing and artificial intelligence. S3, performing feature extraction on the time-frequency spectrogram by the task type extraction network to obtain a task embedding vector, calculating a similarity score, when the maximum similarity score is greater than a set threshold value, outputting a task representation vector, and entering S3, otherwise, inputting features output by the last Transform layer into a classifier; s3, selecting a router according to the task representation vector, selecting a trained expert network by the router, calculating a door control weight, processing universal acoustic features in parallel by the expert network, fusing output of the expert network, fusing deep features and fused features to obtain enhanced features, and enabling the enhanced features output by the last Transform layer to enter a classifier; the invention further provides a system. The problems that the task category autonomous recognition capability is insufficient and the correlation between tasks is ignored are solved.

The invention provides a system for improving positioning precision under a complex underwater acoustic channel and an optimization method, and relates to the field of underwater positioning. The system for improving the positioning precision under the complex underwater acoustic channel comprises a time delay estimation module, a sound velocity profile data module, a sound ray correction module, a slope distance value precision module and a precision improvement module, and the time delay estimation module is mainly used for obtaining more accurate time delay data; the sound velocity profile data module is mainly used for acquiring sound velocity profile data of a positioning area. According to the method, a real sound ray path is estimated by using a sound ray correction method, measurement errors of bubbles in water, sound velocity change and position and propagation time are considered, the positioning precision is further improved, and the comprehensive optimization method enables the system to accurately reflect the real position of a target in a complex underwater environment, so that the positioning accuracy is improved. The overall performance of underwater target positioning is remarkably improved, and the actual requirement of high-precision positioning is met.

The invention relates to the technical field of communication, and discloses a high-precision measurement method for radial velocity of a shallow sea underwater acoustic target based on ray sound field reconstruction. According to the method, a multipath channel joint resolving system is innovatively constructed for the problem that the target speed measurement precision is limited due to a shallow sea complex propagation environment. According to the system, a compound ray model of shallow sea multipath sound propagation is established, and a joint representation equation containing channel impulse response and target motion parameters is constructed by analyzing a time delay-Doppler coupling mechanism of three typical paths of direct waves, water surface reflected waves and water bottom reflected waves. Especially for the multipath interference effect in the shallow sea waveguide environment, the time-frequency domain separation criterion of each path signal is deduced, and a theoretical basis is laid for feature information extraction. The method provides a new technical means for underwater target tracking and positioning, underwater acoustic communication Doppler compensation and marine environment dynamic monitoring, and particularly has an important application prospect in the fields of underwater unmanned cluster collaboration and the like.

The invention discloses a polar region adaptive underwater acoustic positioning correction method based on a multi-dimensional environment coupling model, belongs to the technical field of underwater acoustic positioning, is used for polar region underwater acoustic positioning, and comprises the steps of deploying a heterogeneous sensor network, constructing an environment sensing layer and eliminating a sensor drift error. Establishing a three-dimensional sound velocity model, fusing a vortex kinetic equation, a Kriging interpolation algorithm and an adaptive Runge-Kutta method to track a sound ray trajectory; designing a time-frequency double-domain joint detection algorithm, and combining a lattice IIR filter bank to realize multipath suppression; a 9-dimensional robust UKF algorithm framework is constructed, and multi-source data fusion is realized in combination with federated filtering; and terrestrial magnetism matching and a strain energy density early warning mechanism are integrated. Compared with the prior art, the method improves the positioning accuracy and stability, can more accurately predict the sound propagation path, improves the positioning precision, achieves the dynamic real-time calculation, can quickly and accurately process the continuously changing environment data, and is suitable for the complex polar region environment.

The invention relates to the technical field of underwater communication and positioning, in particular to a multi-mode underwater wireless communication system based on dynamic link calibration, which integrates underwater acoustic positioning guide, attitude adaptive tracking control, optical link enhancement and an error code correction mechanism. Comprising a central control core, an acoustic communication unit, an optical communication transmitting unit, an optical signal receiving unit, an optical control unit, a multi-axis attitude adjusting unit, a data fusion and verification processing unit and a high-speed modulation-demodulation and error code correction unit. And by using a double-shaft servo motor and automatic gain control, real-time target tracking and accurate alignment of optical signals are realized, FPGA and OFDM modulation are combined to optimize data throughput, and a data cross validation mechanism is introduced to correct error codes, so that the data integrity is improved.

The invention provides an underwater positioning system combining sound ray propagation modeling and multipath interference suppression, which relates to the technical field of marine equipment and comprises a self-adaptive underwater sound sensing and environment cognition module, an environment self-adaptive sound ray propagation modeling module, a multipath suppression and direct wave extraction module and a multi-dimensional information fusion and self-adaptive positioning calculation module. The self-adaptive underwater sound sensing and environment cognition module is used for realizing multi-mode sensing and real-time environment feature recognition of underwater sound signals, and the environment self-adaptive sound ray propagation modeling module is used for constructing a dynamically updated sound wave propagation model and providing a propagation path and time constraint for positioning calculation; the multi-path suppression and direct wave extraction module is used for carrying out multi-path signal separation and extracting direct wave features with confidence marks, and the multi-dimensional information fusion and self-adaptive positioning calculation module is used for completing high-precision positioning calculation; according to the system, deep fusion of an environment model and signal processing can be realized, and the positioning precision and robustness of the underwater vehicle in a complex underwater sound environment are remarkably improved.

The invention relates to the technical field of data fusion, and discloses an underwater-based multi-source data feature fusion method, which comprises the following steps: acquiring ocean underwater sound data acquired by a multi-source sensor, preprocessing the ocean underwater sound data, inputting the preprocessed ocean underwater sound data into a trained denoising auto-encoder, extracting initial feature representation, and performing feature fusion on the initial feature representation; optimizing the K-means initial clustering center by adopting an energy valley optimization algorithm to obtain optimal initial clustering center configuration; operating a K-means algorithm based on the optimal initial clustering center configuration, performing clustering analysis on the initial feature representation, and selecting a feature fusion strategy according to a clustering result to obtain a comprehensive feature representation; and outputting a target recognition result through a classification algorithm according to the comprehensive feature representation. And the accuracy, the robustness and the efficiency of underwater target recognition are remarkably improved.

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention provides a visual underwater sensor network simulation method, and relates to the technical field of underwater sensor networks, and the method comprises the steps: configuring network simulation parameters which comprise topological structure parameters, channel parameters and simulation parameters; inputting the channel parameters into an underwater acoustic calculation tool to generate an underwater acoustic multipath channel for simulation; the bit error rate BER of the multi-carrier MFSK underwater acoustic communication algorithm under the underwater acoustic multipath channel condition is calculated, and the bit error rate BER is input into the OTcl script; performing network simulation on the configured OTcl script by using an underwater network simulator; and displaying a network simulation result through a visual interface. The underwater sensor network simulation result can be displayed through the visual interface, the defect that current NS2-based underwater sensor network simulation software does not have a visual interface is overcome, a good man-machine interaction interface can be provided for a user, the development efficiency is improved, and the user is helped to better evaluate the performance of the underwater sensor network.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

Along with increasingly important global marine resources, the problems of marine pollution, ecological damage, natural disasters and the like are increasingly severe, which proposes higher requirements for monitoring and managing the marine environment. A traditional marine monitoring method has a certain effect in practice, but the coverage range is limited, data acquisition is difficult, the processing efficiency is low, and comprehensive monitoring and timely response to a dynamic marine environment cannot be realized. In order to solve the problem, the invention provides the ocean multi-modal data processing method, device and equipment for ocean monitoring and the storage medium, the satellite remote sensing technology, the underwater sound telemetering technology, the buoy observation technology and the shipborne observation technology are integrated, and the advanced big data processing and heterogeneous data fusion method is combined, so that the multi-modal data processing of the ocean is realized. And efficiency is provided for comprehensive monitoring and early warning of the marine environment.

The invention relates to an underwater acoustic image reconstruction method based on angle error compensation, which comprises the following steps of: (1) calculating space coordinate information and a two-way slant range course of a transmitting array element and a receiving array element for each subsystem consisting of the receiving array element and the transmitting array element; (2) calculating a two-way slant range course after phase center approximation; (3) calculating a system function of a distance-Doppler domain; (4) calculating a Doppler phase error; (5) dividing the data into # imgabs0 data blocks which are not overlapped with one another, and compensating micro-range migration errors caused by range errors; (6) obtaining a signal after distance delay error compensation; (7) performing differential range migration correction processing, range pulse compression, secondary range pulse compression and consistent range migration correction processing; (8) carrying out azimuth focusing processing, residual phase error compensation operation and azimuth walking correction processing; and (9) performing coherent fusion processing and inverse Fourier transform to obtain a final high-resolution image. According to the method, the image can be quickly reconstructed, and the real-time processing requirement can be met.

The invention belongs to the field of underwater acoustic networks, and relates to underwater Internet of Things, underwater environment monitoring, resource development and marine ecological protection. In order to provide an overall architecture of a simulation platform and an implementation technology of a core module of the overall architecture, and further enhance the simulation capability of an underlying protocol and a physical channel on the basis of being compatible with a standard network protocol stack and supporting seamless deployment of an existing application program, the invention provides an underwater Internet of Things semi-physical simulation system based on a container. Comprising a terminal host, a gateway node with underwater sound and wired network communication capabilities, and a plurality of real nodes which can be deployed underwater and have underwater sound communication capabilities, and an MAC simulation module and a channel simulation module are deployed on the terminal host and are used for constructing a simulation environment; and the semi-physical simulation interfaces are simultaneously deployed on the terminal host and the gateway node, so that traffic mapping in a simulation environment of the gateway node and the terminal host is realized. The method is mainly applied to design and manufacturing occasions of an underwater Internet of Things simulation system.

The embodiment of the invention provides an air-sea cross-domain communication method and device. The method comprises the following steps: fusing a multi-system communication protocol into a cross-domain communication network protocol through a water surface wireless communication multi-band access technology, and carrying out adaptive networking; the method comprises the following steps: performing dictionary table mapping on original information sent by underwater subsurface buoy sensor equipment to obtain a translated character sequence, constructing a Huffman tree according to the occurrence frequency of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree; the method comprises the following steps: monitoring channel noise and a channel error rate of an underwater acoustic channel in real time, determining a real-time channel state, determining a channel coding parameter adjustment strategy matched with the real-time channel state through an adaptive algorithm, and dynamically adjusting a coding mode and coding parameters of the channel according to the channel coding parameter adjustment strategy. Uploading the Huffman code to a superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters; according to the invention, the air-sea cross-domain communication efficiency can be effectively improved.

The invention discloses an underwater platform data acquisition and transmission system. The system comprises the following steps: an underwater data acquisition module acquires underwater environment information through a heterogeneous sensor and performs standardization processing; the channel modeling module analyzes underwater channel characteristics by using a self-adaptive channel model and generates channel estimation parameters; the channel estimation module is combined with a quantum interference measurement method to correct a channel state and optimize a channel model; the channel optimization and data fusion module constructs a joint feature matching network of underwater acoustic signals and quantum state signals to realize signal fusion and channel feature dynamic optimization; the data transmission optimization module adjusts a data transmission strategy based on the optimized transmission parameters, and the transmission stability is improved; and the energy management module adopts a dynamic power control strategy to realize low-power-consumption operation. According to the method, quantum measurement, sparse Bayesian channel modeling and an adaptive optimization method are combined, and the underwater channel estimation precision, the data transmission efficiency and the system stability are improved.

The invention belongs to the technical field of underwater sound, and discloses an underwater target sound scattering modeling method and system based on a physical information neural network. The method comprises the following steps: establishing a loss function of a physical information neural network by establishing an underwater target acoustic-structure coupling mathematical model; a decoupling parallel dual-network architecture is established, a fluid domain neural network is used for learning and outputting a scattering sound pressure field of a fluid domain, an elastomer domain neural network is used for learning and outputting a displacement field of an elastomer domain, and the two networks realize physical coupling by sharing a sampling point and a corresponding boundary condition loss function at an acoustic-structure coupling boundary; and generating training data, performing iterative training on the decoupling parallel physical information neural network, and calculating a scattering sound pressure field outside the target and a displacement field inside the elastomer by using a trained model. According to the method, the physical information neural network is innovatively applied to the underwater target sound scattering field, and a new thought and a new way are provided for accurately forecasting the underwater target sound scattering field.

The invention relates to the related field of underwater acoustic signal processing, in particular to an underwater acoustic target radiation noise classification method, which constructs an efficient and accurate underwater acoustic target radiation noise classification model through the technologies of lightweight model selection, feature fusion, transfer learning, hyper-parameter optimization, model pruning, quantification and the like. The model not only can realize real-time processing in an environment with limited resources, but also has good classification performance, low energy consumption and high flexibility, is suitable for various application occasions, and particularly has remarkable advantages in an underwater acoustic target classification and real-time monitoring system.

The invention discloses a high-frequency shallow water bottom reverberation signal modeling method, and belongs to the technical field of underwater acoustic engineering and signal processing. The method comprises the following steps: setting a modeling hypothesis; a cylindrical array geometric structure is constructed, scattering units are divided, and channel parameters and scattering functions are obtained in combination with a Bellhop tool and a GABIM model; generating a reverberation signal time domain expression, deducing an array receiving signal spatial domain expression and calculating a spatial spectrum; the model can also adjust underwater environment parameters to realize simulation under different conditions, and performs time domain and space domain theoretical analysis on reverberation signals based on a cylindrical array. Multi-dimensional underwater environment parameters are fused, and the modeling precision is improved; a receiving and transmitting combined cylindrical array is adapted, and spatial gain is embodied; experiments prove that the average error is within 3dB, the practicability is outstanding, support can be provided for design and optimization of the sonar system, and the method is suitable for underwater reverberation signal simulation and characteristic analysis of the sonar system in the high-frequency shallow water environment.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.