2320 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.

The invention relates to the technical field of underwater acoustic communication and signals, and discloses an impulse noise parameter estimation method based on a hybrid neural network, which comprises the following steps of: 1, constructing a nonlinear mapping model of non-Gaussian parameter estimation; 2, establishing a noise sample database; step 3, establishing a noise parameter estimator based on the hybrid neural network; 4, network training and online deployment: respectively carrying out network training through the noise training sample set, storing network parameters after network training, and carrying out online deployment application; step 5, receiving an actually measured noise sample, performing parameter off-line estimation to obtain a noise parameter, and feeding back the noise parameter to a communication receiver; sixthly, the communication receiver designs a signal detector through the received noise parameters, and the signal detection effect under the impulse noise is optimized; according to the scheme, small-sample, rapid and accurate parameter estimation is realized by using the hybrid neural network, and the method is suitable for effective deployment of a noise parameter estimation module of a communication system under impulse noise.

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 discloses an off-network block sparse orthogonal matching pursuit underwater acoustic time delay Doppler domain channel estimation method, and belongs to the technical field of underwater acoustic channel estimation. The method solves the problems that a channel estimation method based on traditional orthogonal matching pursuit cannot estimate frequency shift parameters on non-grid points and is high in calculation complexity. According to the method, channel parameters of integer grid points are obtained based on a BOMP algorithm, and then residual Doppler factors on non-grid points are subjected to further fine estimation based on maximum likelihood criterion fitting. The problem that a traditional OMP channel estimation method in underwater OTFS communication can only estimate channel parameters on grid points is solved, and the estimation precision of channel frequency shift parameters is further improved by estimating frequency shift parameters on non-grid points; and meanwhile, a fractional order dictionary is prevented from being established in a channel parameter process, so that the system complexity is reduced to a certain extent. The method can be applied to underwater sound time delay Doppler domain channel estimation.

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 relates to the technical field of signal buoys, and particularly discloses a signal buoy for a submersible, comprising: a power mechanism comprising a housing and a driving motor, an output shaft of which is connected with a rotating shaft; the heat dissipation assembly comprises an air storage bag, the air storage bag is arranged at one end of the shell in a communicating mode, the outer side of the air storage bag is sleeved with an air bag protection shell, and the air bag protection shell is detachably connected with one end of the shell and provided with a sealing ring; the take-up and pay-off assembly comprises a wire coil and a guide ring, the rotating shaft is sleeved with the wire coil, the guide ring is located above the wire coil, a cable is wound around the wire coil, and the upper end of the cable penetrates through the guide ring and is connected with a floating ball assembly; the floating ball assembly comprises a shell and a communication assembly, the communication assembly is a sonar or underwater acoustic modem, a stuffing box is arranged below the shell, and the upper end of the cable penetrates through the stuffing box and is electrically connected with the communication assembly. According to the invention, high requirements of deep sea exploration and underwater operation on communication tools are met, and more secure and convenient use experience is provided for users.

The invention provides an underwater acoustic target recognition system and method based on multi-modal joint representation. The system comprises a multi-modal feature extractor, a feature fusion module, a fusion feature extractor and a full connection layer. The processing process of the system comprises the steps that multi-modal features based on waveforms and spectrograms are extracted through a waveform network and a spectrogram network of a multi-modal feature extractor respectively, the two-way structural design allows the network to capture features of an underwater acoustic target from different angles, and understanding of signal features is enhanced; the two kinds of feature information of a spectrogram and a waveform are fused through a feature fusion module, and the two kinds of features are fused through a multi-head attention mechanism; and finally, constructing a fusion feature extractor to extract global features. The system disclosed by the invention obtains a very competitive result on two widely used ship radiation noise data sets. Experimental results show that the performance of the system exceeds that of a traditional waveform-based and spectrogram-based model on the premise of keeping a relatively low parameter quantity.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single-beacon Gaussian Newton positioning method and system under a linear sound profile condition. According to the method, underwater acoustic signal propagation time observation noise is modeled into Gaussian distribution, a maximum likelihood estimation problem is constructed, and then the maximum likelihood estimation problem is converted into a nonlinear least square estimation problem; and solving the gradient of the observed quantity to the position coordinates of the AUV based on a chain rule, iteratively solving a nonlinear least square estimation problem based on a Gauss-Newton method, and obtaining the position coordinates of the AUV after iterative convergence. Compared with other single beacon positioning methods which do not consider sound propagation refraction, the method of the invention can obtain a more accurate position calculation result under a linear sound profile condition, and enhances single beacon positioning, especially the practical application capability of the method in deep sea areas.

The invention belongs to the technical field of underwater positioning, and discloses an underwater single beacon virtual long baseline positioning method and system capable of estimating an unknown sound velocity. The method comprises the following steps: constructing a virtual long baseline array by combining stored dead reckoning data; constructing a virtual long baseline positioning problem by combining the relationship between the underwater acoustic signal propagation time and the position and sound velocity of the AUV; auxiliary variables are introduced, a pseudo-linear positioning equation is obtained based on algebraic conversion, and an AUV position initial solution is obtained based on a weighted least square method; and constructing a second-step linear estimation problem by exploring the relationship between the auxiliary variable and the AUV position, and solving by using weighted least squares to obtain a more accurate AUV position estimation solution. Compared with a single-beacon positioning method which does not consider sound velocity unknown, the method provided by the invention can obtain more ideal position estimation performance, and the practical application capability of an underwater single-beacon positioning system is enhanced.

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 discloses an MPSK demodulation and Doppler tracking combined processing method for an underwater sound direct spread system, and belongs to the field of underwater wireless communication and signal processing thereof. The method specifically comprises the following steps: firstly, receiving underwater direct sequence spread spectrum communication signals at the same time by a multi-channel array element of any array, and preprocessing; then, signal synchronization and Doppler coarse estimation are carried out on each array element; carrying out carrier removal on the received signal of each array element to obtain a baseband signal; then, despreading the passband signal of each channel to obtain a despread related signal, and extracting signal peak information; furthermore, after fine Doppler estimation and compensation are carried out on each channel, multi-channel soft value information combination is carried out. And finally, decoding the merged signal by applying BICM-ID to obtain soft value information of each symbol under high-order modulation, and obtaining information bits carried by each symbol through decoding, the underwater communication and measurement joint integrated technology is realized, and the method has certain anti-noise performance.

The invention relates to an underwater acoustic sensor network routing method based on fuzzy logic, which comprises the following steps that: a Sink node periodically broadcasts a HELLO packet to perceive topological change, and each node locally and dynamically maintains a parameter Hcur representing the hop count of the node for representing the hop count from the node to the Sink node; in the relay selection process, the sending node broadcasts a PING packet; after the neighbor node monitors the PING packet, the neighbor node whose hop count to the Sink node is less than or equal to that of the sending node becomes a candidate node, and the candidate node calculates the link quality between the sending node and the node and replies the PONG packet in a point-to-point manner; (4) within the waiting time, the sending node receives the PONG packet replied from the candidate node, and constructs a candidate node routing table; and (5) after the timing of the waiting time is finished, selecting an optimal forwarding node based on a candidate node routing table and a fuzzy system.

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.

The invention discloses an optical fiber silicon-based microstructure underwater acoustic sensor which is a non-intrinsic F-P air microcavity probe with optical signal interference formed by an optical fiber end face and a silicon-based thin film, the characteristic that the silicon-based thin film is sensitive to sound pressure signals is used for picking up sound pressure changes in a liquid sound field environment, the sound pressure changes are converted into the variable quantity of an optical signal interference phase, and the optical fiber silicon-based microstructure underwater acoustic sensor is obtained. Reducing a sound pressure signal in the liquid sound field through a detection demodulation algorithm; the microcavity probe is packaged by using an organic material thin shell structure, so that the hydrostatic pressure resistance of the underwater acoustic sensor is improved to adapt to application in a deep water environment; the optical fiber silicon-based microstructure underwater acoustic sensor comprises an acoustic sensing silicon microstructure, a microstructure sensing probe and a sensor pressure-resistant structure, according to the acoustic characteristic design of the acoustic sensing silicon microstructure, silicon oxide / silicon nitride and other materials are used as sensing materials, and membrane external stress recovery is achieved through internal stress residues. The method is easy to implement in design process, flexible in application and high in environmental adaptability, and can be applied to noise monitoring and target detection in various complex scenes.