211 results about "Multiple signal classification" patented technology

The invention provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, and the method comprises the steps: obtaining the signal data of each frequency band according to the multi-beam spatial domain data received by the shaped antenna and a filter of a frequency domain corresponding to the working frequency band of each beam; performing high-resolution spatial spectrum estimation on the signal data of each frequency band by adopting a MUSIC algorithm to obtain an incoming wave space direction of an interference signal; according to the incoming wave space direction of the interference signal, on the basis of a preset multi-beam zeroing anti-interference strategy, self-adaptive weights of all same-frequency beams are obtained in a spatial domain and frequency domain two-dimensional combined processing mode; the adaptive weights of the same-frequency beams are combined, a comprehensive directional diagram is obtained, and a multi-beam interference zeroing result is obtained, according to the method, the whole airspace is covered with the beams of different frequency points, and space-frequency combined suppression of airspace complex interference is achieved.

The invention provides a millimeter wave radar-based bridge deformation monitoring method and system, and the method comprises the steps: obtaining a transmitting signal and an echo signal of a radar, and carrying out the preprocessing of the signals, and obtaining an intermediate frequency signal; processing the intermediate frequency signal by adopting matched filtering and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by adopting a MUSIC algorithm to obtain target angle information; and obtaining a target distance angle unit by combining the target distance information and the target angle information, and obtaining target deformation information by adopting phase difference and phase unwrapping. According to the invention, the interference of noise and background can be avoided, the deformation displacement of the target area can be accurately calculated, and the real-time monitoring and analysis of the deformation state of the bridge can be realized.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention discloses a sound acquisition and processing system based on cooperation of multiple microphone arrays. The system comprises a sound acquisition module, a multi-channel signal preprocessing module, a sound signal feature extraction module, an abnormal sound recognition module, a sound source positioning module and an alarm module. A multi-channel mixed data signal is collected through a circularly-arranged multi-microphone array formed by a plurality of microphones, after echo cancellation, wave beam domain noise reduction and multi-sound-source separation, a single-sound-source feature vector is extracted, according to the single-sound-source feature vector, abnormal sound including explosion, screaming or glass breakage is recognized through a BiLSTM and an attention mechanism model, and the abnormal sound is recognized through an attention mechanism model. And the GCC-PHAT and MDS-MUSIC algorithms are combined to position abnormal sound production, and alarm information is generated. According to the invention, accurate identification, positioning and alarm of the abnormal sound can be realized, and the real-time performance, the accuracy and the multi-target processing capability of abnormal sound monitoring in a complex environment can be obviously improved.

The invention relates to the technical field of pipeline detection, and provides a non-contact pipeline fluid flow velocity measurement method based on a frequency wavenumber domain spatial spectrogram, and the method comprises the steps: collecting a turbulence signal in a pipeline through a piezoelectric film sensor; carrying out snapshot number segmentation on the turbulence signal to obtain a plurality of segmented turbulence time domain signals; fourier transform and narrowband signal processing are carried out on the segmented turbulence time domain signals, and a spatial spectrum corresponding to each narrowband signal component is obtained; constructing a three-dimensional frequency-wave number domain spectrogram according to the spatial spectrum function; and according to the three-dimensional frequency-wavenumber domain spectrogram, calculating the flow velocity of the pipeline fluid. According to the method, a three-dimensional spectrogram characteristic space is constructed through conjoint analysis of the frequency and the wave number, the angle, distance and frequency characteristics of the turbulence signals are decoupled through spatial spectrum estimation methods such as the MUSIC algorithm, multipath interference and noise are effectively restrained, the flow speed of fluid in a pipeline can be accurately estimated, and limitation in a traditional method is overcome.

The invention belongs to the technical field of array signal processing, and particularly relates to a co-prime array design method based on virtual array hole filling, which comprises the following steps of: S1, forwards moving two sub-arrays of an extended co-prime array by a distance of Nd / 2; s2, turning over one sub-array of the shifted co-prime array to obtain an improved co-prime array; s3, generating a differential virtual array according to the improved co-prime array; s4, introducing and combining the array to carry out hole filling on the differential virtual array; s5, calculating a received signal model and a covariance matrix of the filled virtual array; and S6, DOA estimation is carried out by using an ROOT-MUSIC algorithm. The method has the advantages that better signal source resolution and lower array element mutual coupling are achieved, meanwhile, the continuous freedom degree is improved, more signal sources can be estimated at the same time, and high practical value is achieved.

The invention discloses a single-sensing rotor blade frequency identification method based on a projection method, and belongs to the field of rotor blade non-contact state monitoring. The method comprises the following steps: acquiring a signal based on a leaf tip timing single-sensor sampling mechanism, performing aliasing mechanism analysis of a leaf tip timing single-sensor frequency spectrum on the acquired signal, and determining whether undersampling occurs or not, a spectral line inevitably exists at a corresponding position of a real frequency in the frequency spectrum in the signal; variable rate sampling is carried out through blade tip timing single sensing, and an original signal is obtained; performing time-frequency analysis on original signals obtained by single-sensing variable-rate sampling through a multiple signal classification method to obtain a time-frequency matrix; and projecting each pseudo spectrum of the whole time-frequency matrix to a frequency domain direction through a projection method to obtain a projection frequency spectrum, and carrying out priori-free blade inherent frequency identification based on projection by combining a conclusion of existence of a spectral line to obtain blade kinetic parameters. According to the method, the frequency identification problem of timing under-sampling signals of the lower blade end by only adopting a single sensor is solved.

The invention discloses a DOA estimation method based on multi-moment joint sensing signal enhancement, and the method comprises the following steps: building a sensing channel model for a moving target based on a water surface scene; in order to improve the DOA estimation precision of a water surface object, a time-varying channel estimation result is optimized through multi-moment joint denoising; for time-varying channel estimation, a deep learning algorithm based on Double U-Net is adopted to carry out de-noising in two dimensions of a space domain and an angle domain; and carrying out DOA (Direction of Arrival) estimation by using the de-noised channel matrix and a MUSIC (Multiple USIC) algorithm. According to the method, complex features of the channel are extracted from multi-moment joint perception observation data by adopting the Double U-Net algorithm based on the deep convolutional neural network, channel denoising and enhancement are realized, DOA estimation precision is further improved, and accurate recognition and tracking of water surface objects are effectively realized. Compared with a traditional method, the DOA estimation method provided by the invention has higher estimation precision.

The invention provides a method and system for carrying out DOA estimation by using an antenna array in a partition mode, and the method comprises the steps: 1, dividing the whole antenna array into a plurality of sub-arrays, and enabling each sub-array to independently receive a signal; 2, each sub-array independently collects the received signals, and corresponding filtering and noise reduction preprocessing is carried out; 3, performing independent direction estimation on the signal of each sub-array by using a MUSIC algorithm to obtain a plurality of preliminary DOA values; and step 4, performing comprehensive processing on the DOA preliminary estimation result of each sub-array through a weighted average or joint optimization fusion algorithm to obtain a final DOA estimation value. According to the method, through partition use of the antenna array, the calculation amount of angle estimation of the antenna array can be effectively reduced, so that the real-time performance of DOA estimation is improved, and the positioning precision and the anti-interference capability can be improved by fusing estimation results of a plurality of sub-arrays.

The invention discloses a FDA-MIMO radar multi-target distance angle joint super-resolution method and system, and mainly solves the problem that the existing phased array only depends on the angle to identify multiple targets, resulting in low resolution ability, and the implementation scheme of the method comprises the steps of obtaining FDA-MIMO radar echo data, and performing separation aliasing on the FDA-MIMO radar echo data to obtain a data matrix; performing conversion stacking on the data matrix to obtain NM * L-dimensional data, and calculating a covariance matrix of the NM * L-dimensional data; performing reverse array decoherence processing on the covariance matrix to obtain a new source signal covariance matrix; constructing a two-dimensional spatial spectrum function of a target distance and a target angle for the new source signal covariance matrix through a MUSIC algorithm; and performing spectrum peak search on the two-dimensional spatial spectrum function, and detecting a local maximum value of the two-dimensional spatial spectrum function to obtain a distance and angle joint estimation value of a plurality of targets. The method can improve the angle resolution capability of the target when the target angle interval is lower than the traditional resolution, reduces the influence of the amplitude-phase error on the resolution capability when the target distance interval is large enough, and can be used for target recognition.

The invention belongs to the technical field of signal processing, and particularly relates to an under-snapshot 2D-MUSIC direction finding method based on spatial translation invariance. According to the method, the space translation invariant characteristic of a uniform linear array or a uniform planar array is utilized, a calculation method of a forward and backward space-time cross-correlation function is provided, a pseudo covariance matrix is constructed by using the space-time cross-correlation function based on the Hermitian-block Toeplitz characteristic of a signal ideal covariance matrix, then a MUSIC spatial spectrum is constructed, and the wave direction is extracted. Compared with a traditional 2D-MUSIC algorithm, the method still can maintain the high-precision angle estimation capability under the condition that the number of snapshots is limited, and does not greatly improve the calculation amount. Compared with a spatial smoothing MUSIC algorithm, the method does not need to divide sub-arrays, fully utilizes the full array aperture, and avoids the loss of resolution.

The invention provides an arrival wave angle estimation method based on a zero scanning leaky-wave antenna. A rectangular waveguide is selected as a basic structure to design a periodic leaky-wave antenna only working in a-1 harmonic state; designing necessary antenna simulation parameters; a first leaky-wave antenna is designed; adjusting the gap interval p, and designing a second leaky-wave antenna; obtaining pattern functions of the first leaky-wave antenna and the second leaky-wave antenna; proper amplitude and phase parameters are selected for directional diagram synthesis, and a new directional diagram function is obtained; constructing an array guiding matrix suitable for the MUSIC algorithm; signal sources are arranged at different positions, simulation testing is carried out through MATLAB, and the incoming wave angle estimation capability of the zero-point scanning leaky-wave antenna is verified. According to the invention, a feasible technical path is provided for high-precision positioning and tracking application in future 6G mobile communication. The antenna is especially suitable for application in millimeter wave and terahertz frequency bands, such as vehicle-mounted communication, unmanned aerial vehicle platforms, military electronic countermeasures and the like.

The invention discloses a cable partial discharge source positioning method and system based on an improved MUSIC algorithm, and relates to the technical field of power system insulation detection and fault diagnosis. According to the method, the time-frequency domain feature enhancement processing is introduced before the MUSIC algorithm, so that the method can adapt to the non-stationary characteristic of the partial discharge signal, and the spectral peak resolution under the condition of low signal-to-noise ratio is remarkably improved. Compared with the prior art, the method has the advantages of being high in anti-noise performance, high in positioning precision and simple in implementation mode, is suitable for power cable partial discharge online monitoring and fault diagnosis, and can be popularized and applied to insulation defect positioning of high-voltage equipment such as a transformer and a switch cabinet.

A static human pose estimation method based on channel state information (CSI) signal angle of arrival (AoA) estimation is disclosed. A signal angle of arrival in a sensing area is estimated from CSI and an image is constructed, and a human pose is estimated from the image using a teacher-student network. The method comprises the following specific steps: CSI signals in a sensing area are collected at different heights using a receive antenna column with a moving track. Two-dimensional AoA image features are constructed: CSI information is converted into one-dimensional AoA data by using a MUSIC algorithm, and the one-dimensional AoA data of different heights are combined into a two-dimensional AoA image, and an environmental denoise algorithm is designed. A teacher-student network model is constructed, and a student network based on the two-dimensional AoA image is supervised, wherein the student network model can independently estimate a human pose from CSI.

A multi-carrier signal angle and time delay estimation method based on a nested array belongs to the field of wireless communication, and comprises the following steps: firstly, designing a nested array set according to the number of radio frequency links and the number of pilot frequency carriers, and designing a beam forming matrix into a random banded Toeplitz matrix; meanwhile, selection of pilot frequency carriers is determined according to the nested array of the pilot frequency carriers. At a transmitting end, each pilot carrier transmits a unit matrix signal in each pilot block period time, at a receiving end, the signal received by each pilot carrier is vectorized, and all pilot carrier vectors are gathered to form a new pilot vector. The method comprises the following steps of: calculating a covariance of a new pilot frequency vector, selecting a maximum continuous holeless vector, improving a rank of the holeless vector by using a smoothing technology, finally solving a time delay, a departure angle and an arrival angle of each path by using a 3-D ESPRIT algorithm, and pairing all the time delays, departure angles and arrival angles through a MUSIC algorithm. After the method is used for processing data, a super-resolution method 3-D ESPRIT can be used for time delay and angle estimation, and the angle and time delay estimation precision is effectively improved.

The invention provides a 2D-MUSIC beam line damage positioning method and system, and relates to the technical field of material safety monitoring. By collecting reference guided wave signals at different temperatures, amplitude-phase changes of direct waves of all sensor channels are analyzed, and a temperature-induced amplitude-phase error matrix is constructed; the error matrix is introduced into a two-dimensional multiple signal classification (2D-MUSIC) algorithm, an ideal steering vector is corrected, and a spatial spectrum under an actual propagation model is reconstructed; a cost function is established in combination with noise subspace characteristics, and high-precision and high-robustness positioning of tiny damage is realized through iterative optimization and synchronous inversion of damage positions and channel error parameters. According to the method, the applicability and the positioning accuracy of the 2D-MUSIC algorithm in a variable-temperature environment are remarkably improved.

The invention discloses an unmanned aerial vehicle coherent signal direction finding method based on a uniform circular array, and relates to a radio direction finding technology, and the method comprises the steps: defining related parameters, and receiving an unmanned aerial vehicle signal through the uniform circular array; preprocessing the matrix T, and converting a uniform circular array in an original array element space into a virtual uniform linear array in a mode space; defining a vector z, and constructing a response Toeplitz matrix through the vector z; on the basis of the transformed signal, a covariance matrix of a corresponding Hermitian Toeplitz matrix reconstructed from the observation space is calculated; according to the calculated covariance matrix, defining a MUSIC algorithm to search a maximum spectrum peak; and converting the spectral peak estimation of the MUSIC algorithm into a polynomial by using the ROOT-MUSIC algorithm, so as to determine the estimation of the direction of arrival by using the root of the polynomial. According to the method, the defect of direction finding of the unmanned aerial vehicle coherent signals by a traditional direction finding method is overcome, successful separation of the unmanned aerial vehicle coherent signals is ensured, and the direction finding precision of the unmanned aerial vehicle coherent signals is improved.

The invention provides a high-voltage electricity approaching safety early warning method, system and device for an automobile crane, and belongs to the technical field of engineering machinery safety protection. Comprising the steps of collecting an electric field signal radiated by a high-voltage line, reversely deducing a distance based on an electric field intensity attenuation model, and calculating an azimuth angle of a charged body through a MUSIC algorithm; controlling a laser radar to perform high-density scanning within a set angle according to the azimuth angle to obtain three-dimensional point cloud data of the high-voltage line; time synchronization and coordinate registration are carried out on the electromagnetic sensing array and laser radar data, a three-dimensional space model of the high-voltage line is established after fusion, and the shortest distance between the three-dimensional space model and a suspension arm is calculated; identifying a voltage level, calculating a basic safety distance, correcting by combining an environment factor, and multiplying by a safety coefficient to obtain a dynamic safety threshold value; and according to the real-time distance between the suspension arm and the electrified body, triggering graded early warning and control actions. According to the invention, the problem of measurement deviation of a single sensor under complex electromagnetic environment, changeable illumination and meteorological working conditions is solved.

The invention discloses a narrow-band-based smooth precession cone micro-motion extraction and scattering point identification method, and the method comprises the steps: building an equivalent scattering point model of a smooth precession cone target based on a narrow-band radar, estimating the number of echo signals, carrying out the order determination of a sliding window Pole Root-MUSIC algorithm, setting the window length, and carrying out the recognition of the scattering points of the smooth precession cone target. Estimating a micro-Doppler time-frequency curve of a target scattering point through a sliding window, carrying out IRadon transformation, positioning and identifying an energy gathering condition, distinguishing the micro-Doppler time-frequency curve of a cone top scattering point from the micro-Doppler time-frequency curve of a cone bottom scattering point, and realizing scattering point identification; and then Radon transformation is respectively carried out to obtain two corresponding trend lines, and the micro Doppler time-frequency curves of the target scattering points are respectively correlated and smoothed according to the trend lines to realize micro-motion feature extraction of the target. Under the condition of narrow-band radar observation, the micro-Doppler time-frequency curve can be directly and quickly obtained, and correlation is completed.

The invention relates to a direction-of-arrival estimation method based on a linear-nonlinear branch fusion neural network, and the method comprises the steps: obtaining an original signal of an antenna array, calculating a reference empirical covariance matrix based on the original signal, and obtaining an input tensor based on the reference empirical covariance matrix; based on the input tensor, processing by using a dual-path neural network to obtain a residual matrix; the dual-path neural network comprises a linear branch and a nonlinear branch; obtaining an enhanced covariance matrix based on the residual matrix and the reference empirical covariance matrix; and based on the enhanced covariance matrix, obtaining a DOA estimation value by using a Root-MUSIC algorithm. Compared with the prior art, the direction-of-arrival estimation method provided by the invention has the advantages of a traditional subspace algorithm and a neural network participation type method.

The invention relates to a three-dimensional MUSIC sound source localization method based on an improved particle swarm optimization algorithm, and belongs to the technical field of sound source localization, and the method comprises the following steps: S1, building a covariance matrix for sound signal data collected by an array of M array elements, and separating a noise subspace; s2, performing array manifold construction of a three-dimensional space by adopting a large-step azimuth angle, a large-step pitch angle and a large-step distance; s3, performing preliminary positioning on the sound source according to a MUSIC algorithm principle; and S4, initializing parameters of the particle swarm optimization algorithm for alternate search, and searching a three-dimensional space.

The invention provides a near-field information source positioning method and system. The method comprises the following steps: on the basis of a direction of arrival (DOA) and a receiving signal model in a near-field environment, creating a fourth-order accumulation matrix only containing DOA information; performing eigenvalue decomposition on the fourth-order accumulation matrix to obtain a strong signal subspace; bringing the strong signal subspace into the noise subspace to form a first extended noise subspace; a DOA representing a weak signal source is determined by using a MUSIC algorithm and the first extended noise subspace; creating a covariance matrix of the received signal; carrying out eigenvalue decomposition on the covariance matrix and expanding a noise subspace to construct a two-dimensional spatial spectrum; the DOA of the weak signal source is input into the two-dimensional spatial spectrum for one-dimensional search to obtain the positioning information of the weak signal source, and the positioning information comprises the distance of the weak signal source, so that the accuracy of positioning the near-field signal source in a scene where strong and weak signals coexist is improved.

The invention provides a continuous human body activity identification method based on FMCW radar multi-domain feature fusion, and the method comprises the steps: processing original data of a radar through employing a fast Fourier transform algorithm and a multi-signal classification technology, and obtaining a distance-Doppler image and a distance-angle image; the radar data are mapped to a fractional domain through short-time fractional Fourier transform, and a fractional domain spectrogram is obtained; then realizing synchronous segmentation of three types of continuous activity sequence domain information through a variable window length STA / LTA continuous action detection algorithm; and finally, taking the segmented activity sample as the input of a multi-input multi-task continuous activity recognition model, extracting input features through a convolutional network and a bidirectional long-term memory network, introducing a CTC layer to fuse multi-input representation, calculating CTC loss of a prediction sequence and a real label, executing multi-task learning, and obtaining a human body action recognition result. According to the method, continuous human body actions can be segmented at high precision, three types of radar domain information are accurately fused, and a high-precision continuous human body action recognition task is realized.

This invention discloses a DOA estimation method based on the anti-diagonal line of the second-order statistical matrix of a non-circular near-field quasi-stationary signal, belonging to the field of array signal processing. Addressing the difficulty in discerning DOA estimation for non-circular near-field quasi-stationary signals when the source angle difference is small, this invention reconstructs a virtual equivalent signal by using the anti-diagonal line of the pseudo-covariance matrix and the parallel lines of the anti-diagonal line of the covariance matrix, thus achieving DOA estimation for non-circular near-field signals. The method implementation steps include: calculating the covariance matrix and pseudo-covariance matrix of the signal at each time frame of the quasi-stationary signal; extracting the parallel lines of the anti-diagonal line of the covariance matrix and the elements of the anti-diagonal line of the pseudo-covariance matrix, reconstructing them into an equivalent signal; and using the classical MUSIC algorithm to estimate the parameters of the equivalent signal. This scheme avoids the spectral peak overlap problem that occurs when the source angle difference is small, improving the accuracy of DOA estimation.

The application discloses a two-dimensional Root-MUSIC angle automatic pairing method and system based on signal receiving timing, and specific steps of the method are as follows: step 1, a Root-MUSIC algorithm mathematical model suitable for angle estimation of two orthogonal arrays of an L-shaped array is constructed, the Root-MUSIC algorithm is applied to the two orthogonal arrays respectively, and a set of azimuth angles and a set of elevation angles are estimated; step 2, timing detection and dynamic grouping are carried out based on signal arrival timing, and simultaneous multi-signal receiving processing is converted into a single-signal processing flow organized according to timing; the Root-MUSIC algorithm is executed on each group of signals, and pairing of the azimuth angles and the elevation angles is automatically completed; and step 3, the pairing result of step 2 is stored, and whether the pairing process is ended is judged, if yes, the angle pair is output in sequence. The application constructs a joint processing mechanism of timing detection-dynamic grouping-serial processing, converts traditional multi-dimensional matching calculation into a simple timing alignment operation, and makes angle pairing become a natural derivative result of the signal processing flow.

This invention provides an adaptive method and system for fast spatial spectrum estimation of multipath channels. The method includes: S1, using a linear uniform array as the receiving array, with the first element in the array as the reference element, deriving the broadband signal model and steering vector matrix received by the array; S2, constructing a fitness function, and using the dung beetle algorithm to search for the optimal fractional Fourier transform order of the received signal from the reference element; S3, performing the optimal fractional Fourier transform on the received signal of the array; S4, finding the coordinates (u) corresponding to the maximum value. p ,v p S5. Energy detection: Set a threshold for comparison. If the threshold is met, it indicates that the location of this point is a component of the multipath; otherwise, the algorithm ends. S6. Use the CLEAN algorithm to separate the multipath components. The center frequency of its narrowband filter is u. p S7. Use the MUSIC algorithm to estimate the incident angle of the multipath components; S8. Mask the strongest component among the current multipath components and return to repeat steps S4 to S7 until all components are estimated.

The application provides a kind of intelligent metasurface assisted near-field time division integrated sensing method and system, it is related to wireless communication field.The application designs integrated sensing system by introducing RIS and single antenna receiver, avoids complex full-duplex base station and multi-antenna receiver, significantly reduces hardware complexity and implementation cost under the premise of ensuring high-quality communication and sensing performance;The adjustable frame structure designed separates the communication subframe and the sensing subframe completely, decouples the communication beam optimization and the sensing signal design, respectively realizes the communication and sensing functions in a purer signal environment;In the communication subframe, the communication and rate maximization are realized, and in the sensing subframe, the sensing capability is enhanced through the transmission beam pattern matching, which significantly improves the overall communication rate and sensing accuracy of the system;Finally, a virtual multi-dimensional receiving signal of multiple passive targets in the near field of RIS is constructed through repetition coding design, and then the 2D-MUSIC algorithm is used to estimate the target angle and distance parameters.

The invention provides a multi-sound-source positioning method and system and electronic equipment, and relates to the technical field of sound source localization, and the multi-sound-source positioning method comprises the steps: obtaining time domain sound pressure collection data of a multi-channel microphone array; performing short-time Fourier transform on the time domain sound pressure acquisition data to obtain a frequency domain sound pressure signal model; performing spherical Fourier transform on the frequency domain sound pressure signal model to obtain a spherical harmonic domain sound pressure coefficient model; processing the spherical harmonic domain sound pressure coefficient model to obtain a high-order cumulant matrix corresponding to the spherical harmonic domain receiving signal; and performing singular value decomposition on the high-order cumulant matrix corresponding to the spherical harmonic domain receiving signal to obtain a noise subspace. According to the method, the system and the electronic equipment provided by the invention, the dimensionality of the steering vector matrix is expanded through the spherical harmonic domain MUSIC algorithm of the fourth-order cumulant, so that the effective rank of the steering vector matrix is improved, and the number of locatable target sound sources is increased.

The application discloses a non-uniform noise background-based sparse array gridless parameter estimation method, and the implementation steps are as follows: a sparse array structure is arranged at a receiving end; a sparse array receiving signal in a non-uniform noise background is modeled; a covariance matrix of the array receiving signal is calculated; receiving data of a virtual differential common array is calculated through vectorization and de-redundancy; a distribution to which a fitting error is subjected is derived, and a parameter estimation optimization model based on atomic norm minimization and avoiding a complex regularization parameter determination process is constructed; a semi-positive definite programming form of a primal problem and a dual problem is derived; and a wave direction estimation result is obtained through a MUSIC algorithm or polynomial root seeking. In addition, on the basis of fully utilizing a virtual differential common array of the sparse array to provide extended degrees of freedom and all information, the application restrains the influence of non-uniform noise by constraining the sparsity of data atoms to be recovered and selecting a regularization parameter from a chi-square distribution table according to a distribution to which a fitting error between the data to be recovered and actual receiving data not affected by noise is subjected, overcomes the grid mismatch problem caused by parameter domain discretization, avoids a complex process of determining the regularization parameter, and can obtain a higher-precision source direction estimation result.

The invention discloses a hydrophone array multi-sound-source positioning method based on a frost ice optimization algorithm, and belongs to the field of sound source positioning. According to the method, a linear hydrophone array is constructed, an underwater sound source positioning scene is set, and a sound source signal is collected and processed to obtain a receiving signal; a MUSIC algorithm is used to process the received signal, a spatial spectrum is generated to preliminarily estimate information such as an azimuth angle and a pitch angle of a sound source, normalization processing is carried out on the spatial spectrum, a gravitational force area and a repulsive force area are divided, and a gravitational force-repulsive force type artificial potential energy field is constructed; an independent sub-population is initialized for each sound source by adopting a frost ice RIME optimization algorithm, individual positions are updated in combination with an artificial potential energy field and a frost puncture mechanism in an iterative updating process, and particles with the highest fitness are selected as final estimation positions through iterative search. The method overcomes the limitation of a traditional optimization algorithm, remarkably improves the precision and reliability of sound source positioning, and is particularly suitable for a multi-sound-source positioning scene in a complex underwater sound environment.