77 results about "Multiple signal classification" patented technology

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 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 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 application provides a FDA-MIMO radar gridless target parameter estimation method based on a single base plane array, relates to the technical field of array signal processing, constructs a single base plane array FDA-MIMO radar system, and the transmitting end and the receiving end of the radar system are composed of a uniform plane array; a receiving signal model is established based on the radar system, the model is vectorized, a selection matrix is introduced to construct an angle-distance decoupling receiving signal model; the covariance matrix of the receiving signal model is calculated; a low-rank matrix reconstruction optimization problem suitable for the model is established according to the covariance matching criterion; the optimization problem is solved by the idea of alternating projection by using the properties of the covariance matrix; the information of the angle theta and the distance r is estimated by the multiple signal classification method according to the covariance matrix; the problems that the FDA-MIMO radar angle-distance decoupling model in the prior art is only suitable for a linear array, and the target parameter estimation method has low estimation accuracy and high calculation complexity can be solved.

The application discloses a DOA estimation method and system based on a generative adversarial network. The method first acquires time-domain coherent signals received by a uniform linear array, performs preprocessing, and generates a coherent signal covariance matrix and a corresponding incoherent covariance matrix. Secondly, a covariance matrix reconstruction model is constructed, based on the coherent signal covariance matrix and the corresponding incoherent covariance matrix, a generator of the generative adversarial network is used to extract deep features from the preprocessed coherent signal covariance matrix, and the coherent signal covariance matrix is reconstructed. Then, the MUSIC algorithm is used to estimate and verify the DOA based on the reconstructed covariance matrix. The application improves the precision of coherent DOA estimation while not losing the utilization rate of the array aperture. The application has less parameter quantity, lower calculation complexity, faster covariance matrix reconstruction speed and stronger covariance matrix reconstruction capacity.

The invention relates to a phase shift keying signal positioning method based on co-prime sum-difference common-array compensation. Direction of arrival estimation wide in estimation range and high in estimation precision is achieved. After a non-circular signal is received through an array antenna of an augmented co-prime array structure, the non-circular signal is subjected to digital processing to obtain a digital signal, and an augmented co-prime array comprises a first sub-array and a second sub-array; carrying out covariance matrix calculation on the digital signal and vectorizing a covariance matrix to obtain a difference comatrix and a sum comatrix; determining redundant array elements according to the structure that the array elements in the augmented co-prime array are removed without influencing the difference coordination array, and repositioning the redundant array elements to obtain a third sub-array and a fourth sub-array; translating all the sub-arrays by an optimal shift distance to obtain a converted augmented co-prime array structure; and estimating the direction of arrival by adopting the MUSIC algorithm again according to the conversion augmented co-prime array to obtain the direction of arrival.

This invention discloses a method and system for separating AIS aliasing signals using a four-circular array antenna. The steps are as follows: S100: AIS signals are received through multiple antennas to obtain the spatial baseband signal matrix X, and the phase error Δφ is calculated using interval calculation. k S200: Calculate the phase compensation matrix p; S200: Compensate the baseband signal according to the phase compensation matrix to obtain the baseband signal X. comp S300: For baseband signal X comp By expanding the spatial and temporal dimensions, we obtain the spatiotemporal snapshot matrix X. st (t); S400: The covariance matrix R is obtained by estimating the spatiotemporal snapshot matrix. xx S500: For the covariance matrix R xx Eigenvalue decomposition is performed to obtain the signal subspace and noise subspace; S600: DOA estimation is performed using the MUSIC algorithm to construct the spatial spectrum P(θ); S700: The beam w at the optimal solution is obtained using the space-time constrained minimum variance algorithm; S800: The separated signal is output through the sparse separation method of protocol features. This method can significantly improve the availability of AIS self-organizing network and provide more reliable protection for ship navigation safety.

A system and method for determining the distance between two wireless network devices is disclosed. The present system utilizes an algorithm that leverages additional information contained in the pseudo-spectrum generated the MUSIC algorithm. Specifically, since the MUSIC algorithm operates using the square of the channel frequency response, additional peaks are created in the pseudo-spectrum, which can be used to verify the correctness of the distance measurement. The algorithm verifies that the first peak is correct by identifying a second peak and looking for an additional peak located within a search region that is determined based on the first two peaks.

The invention provides an angle capturing method based on a two-dimensional MUSIC algorithm and related equipment, and relates to the technical field of wireless communication. The method comprises the following steps: receiving a sampling signal and a radio frequency signal, and obtaining a plurality of snapshot data according to the radio frequency signal; calculating a covariance matrix of the radio frequency signal based on the snapshot data, performing eigenvalue decomposition on the covariance matrix, and determining a signal subspace and a noise subspace according to the obtained eigenvalue; calculating a two-dimensional MUSIC spatial spectrum based on the noise subspace; calculating a dynamic threshold, and obtaining a plurality of candidate angles according to the dynamic threshold; performing image rejection processing on each candidate angle to obtain an effective candidate angle, accumulating the effective candidate angle into a two-dimensional statistical histogram, and processing to obtain a two-dimensional probability distribution diagram; selecting a first estimation angle from the two-dimensional probability distribution map; and determining an angle capture state of the target based on the first estimated angle and a second estimated angle at the previous moment. By adopting the method, the angle capturing accuracy can be improved.

The application relates to a MUSIC direction-finding method based on an underwater information system cross array, and belongs to the technical field of underwater acoustic direction finding. In view of the problems of low signal-to-noise ratio and low direction-finding precision of underwater signal direction finding, the application provides a MUSIC direction-finding method based on an underwater information system cross array. The method is based on a MUSIC algorithm, frequency extraction and spatial spectrum accumulation, a cross array is used to perform direction-finding processing on a signal, the relative position relationship between the signal and the cross array is obtained, a suitable linear array in the cross array is selected to perform direction-finding processing on the signal, and the direction finding of an underwater target acoustic signal under low signal-to-noise ratio can be effectively realized.

The invention discloses a sparse array DOA estimation method based on a residual network, and the method comprises the following steps: reconstructing an ideal Here-Toeplitz covariance matrix of a virtual uniform linear array through deep learning, and achieving the end-to-end training in combination with the differentiability of a Root-MUSIC algorithm. By introducing the multi-delay autocorrelation matrix tensor and residual learning, the method can effectively process challenging scenes such as coherent signals, a small number of snapshots and broadband signals. The effectiveness of the method is verified in the embodiment, high-precision estimation can be achieved under various conditions, the target that the number of estimated information sources is larger than the number of physical array elements is successfully achieved, and a new solution is provided for sparse array DOA estimation.

The invention discloses a 5G communication modulation signal identification method and system, and belongs to the technical field of signal identification, and the 5G communication modulation signal identification method comprises the steps of signal receiving, spatial feature separation, multi-domain feature extraction and feature fusion and identification. According to the invention, spatial spectrum estimation is carried out on the multipath beam superposition signals received by the MassiveMIMO array by using the MUSIC algorithm, efficient separation of N paths of independent beam signals is realized, and the technical problem that a traditional single-antenna model cannot adapt to a multi-antenna scene is thoroughly solved; according to the method, the sparse Transform model and the stacked self-organizing mapping are combined, accurate classification and proportion calculation of QPSK, 16QAM, 64QAM and 256QAM hybrid modulation signals are achieved, the recognition accuracy is larger than or equal to 92% when SNR = 0dB, the recognition accuracy is improved by more than 18% compared with a traditional algorithm, and the problems that high-order modulation constellation points are fuzzy and hybrid modulation classification is difficult are effectively solved.

The application discloses a high-resolution angle measurement method based on target decoupling, and belongs to the field of radar signal processing. Firstly, the direction of arrival of a target is measured preliminarily, and then it is judged whether the main lobe formed by the target is composed of one target or two targets with similar angles, if the main lobe is composed of one target, the angle measurement result is directly output; if the main lobe is composed of two targets, the two targets are searched again by using a decoupling algorithm, so that the two targets with similar angles are identified. The application uses the reconstructed received signal vector to judge the coupling of the target searched preliminarily, and defines a two-dimensional angle spectrum to decouple the target, so that the angle resolution is improved, the calculation complexity is low, and compared with the MUSIC algorithm, the two similar target azimuth angles that cannot be distinguished by the MUSIC algorithm can be distinguished.

The application provides a high-degree-of-freedom DOA estimation method based on a mirror image co-prime array in a pulse noise environment, and relates to the technical field of array signal processing, and comprises the following steps: S1: obtaining an observation signal by relying on non-circular signal characteristics; S2: calculating a pseudo-covariance matrix based on the information of the observation signal and a square-based correlation entropy operator to suppress the pulse noise; S3: vectorizing the pseudo-covariance matrix to obtain a virtual vector, removing redundant rows in the virtual vector, and performing truncation and sorting according to positions corresponding to continuous virtual array elements to obtain a virtual array receiving signal; S4: performing spatial smoothing on the virtual array receiving signal to obtain a full-rank reconstructed covariance matrix; S5: obtaining a noise subspace by performing eigenvalue decomposition on the covariance matrix; and S6: obtaining an accurate DOA estimation value by performing spectral peak searching on the noise subspace by using a MUSIC algorithm. The method has a high degree of freedom, can accurately complete DOA estimation in a pulse noise environment, and has good performance.

The application discloses a multi-path time delay estimation method based on orthogonal linear frequency modulation wave division multiplexing signals, and the process is as follows: an OCDM signal is transmitted by a transmitting end, a time domain receiving signal is obtained after the OCDM signal passes through a multi-path channel, a fractional domain Fourier transform (FrFT) is performed on the time domain receiving signal to obtain a fractional domain receiving signal, a covariance matrix of the fractional domain receiving signal is calculated after the fractional domain receiving signal is de-coherent, a subspace of the covariance matrix is decomposed and a spatial spectrum is calculated by using a MUSIC algorithm, and a time delay corresponding to a spectrum peak of the spatial spectrum is a time delay estimation value. The multi-path time delay estimation method disclosed by the application can accurately estimate a time delay of a dense multi-path channel with a Doppler effect, effectively reduces a relative root mean square error of time delay estimation, is still relatively accurate in estimating a time delay when the multi-path is dense, and has a certain resistance to a Doppler frequency offset.

The application discloses a kind of even circle array one-dimensional spatial spectrum direction finding signal processing device, including data processing and covariance matrix module, MUSIC algorithm processing module, Capon algorithm processing module, processing result fusion module and DML algorithm processing module;The function of data processing and covariance matrix module is received signal pre-processing, and covariance matrix is obtained by calculation;MUSIC algorithm processing module is calculated using input covariance matrix to obtain the MUSIC spatial spectrum of receiving array;Capon algorithm processing module is calculated from covariance matrix to obtain the Capon spatial spectrum of receiving array;Processing result fusion module analyzes and judges the calculation result of MUSIC spatial spectrum and Capon spatial spectrum, whether MUSIC spatial spectrum needs iterative search, obtains the direction finding result of incoherent signal, and judges whether it contains coherent signal;DML algorithm processing module processes a pair of coherent signal DOA calculation, and outputs spatial signal direction finding result.This device can accurately calculate the DOA of incoherent signal;And on the basis of incoherent signal direction finding, a pair of coherent signal direction finding is completed.

The invention provides a multi-antenna angle scanning and capturing method and related equipment, and relates to the technical field of wireless communication. The method comprises the following steps: receiving electromagnetic wave signals about a target sent by a plurality of antennas, and carrying out signal preprocessing to obtain multiple paths of baseband complex signals; performing angle scanning on the multiple paths of baseband complex signals by adopting a MUSIC algorithm to obtain an angle spectrum containing a plurality of spectrum peaks, and extracting a plurality of candidate angle values from the angle spectrum; determining a corresponding sector range around each candidate angle value, and executing tracking scanning on each sector range in parallel within a preset time to obtain a power metric value of each sector range; and performing time smoothing processing on the power metric value of each sector range to obtain a smooth power value of each sector range, and when a target sector range of which the smooth power value meets a preset condition exists, performing angle scanning in the target sector range to obtain a target angle value. By adopting the method, the real orientation of the target can be identified.

The application discloses a deep learning assisted subspace plane array DOA estimation method, relates to the technical field of satellite navigation and artificial intelligence, and realizes high-precision DOA estimation and anti-interference in a hardware resource limited scene by combining light-weight deep learning and an optimized subspace method. First, a plane array receives a signal and calculates a covariance matrix, then a light-weight CNN-Transformer network is used to perform real-time processing on the covariance matrix, and a joint probability distribution of the elevation angle and the azimuth angle is output to provide a coarse estimation; then, a dynamic weighting strategy is used to enhance the covariance matrix, a fast MUSIC algorithm is executed in a local region guided by deep learning to realize fine DOA estimation; finally, a projection method is used to generate a null in the interference direction while maintaining the gain of the navigation signal direction, and anti-interference beam forming is completed. The application solves the problems of DOA estimation and anti-interference of a small plane array under a low SNR by combining the innovation of deep learning guided subspace search and the projection method beam forming and by synergistic optimization of a hybrid architecture, and has high precision and low resource consumption.

A method for jointly estimating the direction of arrival and polarization parameters of an electromagnetic wave signal, comprising the following steps: (1) establishing a uniform linear array with dual-fed orthogonal linear polarization units as array elements; (2) sampling the received signal to obtain the received signal matrix Z; (3) calculating and performing eigenvalue decomposition to calculate the variance; (4) generating a pseudo-noise matrix with zero mean and variance; (5) superimposing the two signals to obtain the matrix; (6) calculating and performing eigenvalue decomposition; (7) obtaining the parameter estimates according to the Root-MUSIC algorithm. The beneficial effect of this invention is to improve the parameter estimation accuracy of the array in a low signal-to-noise ratio environment. This invention can be applied to the field of parameter estimation technology in signal processing.

This invention proposes a collaborative uplink / downlink channel extrapolation method for large-scale MIMO systems. Addressing the issues of limited uplink pilot bandwidth and uplink / downlink timing asynchrony in time-division duplex systems, this method proposes a collaborative channel reconstruction scheme between the User Equipment (UE) and the Base Station (BS). Specifically, the method includes: at the UE, extracting downlink propagation delay parameters from a small number of high-energy downlink beam observations using a smoothed MUSIC algorithm, and feeding back only the low-dimensional delay parameters to the base station; at the BS, estimating the uplink / downlink delay offset using uplink narrowband pilot observations, and calibrating the fed-back delay parameters; at the BS, using complementary subspace projection technology, further estimating the lost delay paths in the downlink observations from the uplink observations, and merging them to obtain a complete delay set; based on the complete delay set, reconstructing the full-band uplink channel using a compressed sensing algorithm. This method significantly reduces computational complexity while maintaining channel extrapolation accuracy.

The application provides a blind user positioning method based on a jammer cluster, and relates to the field of positioning, and the method comprises the following steps: the jammer cluster acquires low-orbit satellite signals at different time points; for the same time point, the MUSIC algorithm is used to calculate and predict the low-orbit satellite signals; a space cone is constructed, with the jammer as the cone top, the satellite-jammer direction as the axis, and a preset half-cone angle; a projection prediction circle of the space cone and the ground plane is obtained; the intersection area of the projection prediction circles at different time points is determined as the area where the ground target is located, and the positioning result of the ground target is obtained by calculating the centroid. In the above method, the low-orbit satellite signals received by the jammer cluster are calculated and predicted, a prediction area is formed on the ground, then the centroid is calculated by using the Monte Carlo algorithm, and the prediction position of the ground target is obtained, so that the non-cooperative positioning under special conditions such as limited network conditions and remote areas is realized, and the positioning effect and instantaneity are good.

The application discloses a DOA estimation method and system based on a generative adversarial network. The method first acquires time-domain coherent signals received by a uniform linear array, performs preprocessing, and generates a coherent signal covariance matrix and a corresponding incoherent covariance matrix. Secondly, a covariance matrix reconstruction model is constructed, based on the coherent signal covariance matrix and the corresponding incoherent covariance matrix, a generator of the generative adversarial network is used to extract deep features from the preprocessed coherent signal covariance matrix, and the coherent signal covariance matrix is reconstructed. Then, the MUSIC algorithm is used to estimate and verify the DOA based on the reconstructed covariance matrix. The application improves the precision of coherent DOA estimation while not losing the utilization rate of the array aperture. The application has less parameter quantity, lower calculation complexity, faster covariance matrix reconstruction speed and stronger covariance matrix reconstruction capacity.

The application discloses a meshless frequency estimation method for layout unconstrained blade tip timing signal and belongs to the technical field of array signal processing. The method comprises the following steps: setting a sampling model; setting the sampling model of the irregular layout of the blade tip timing signal according to the basic principle of the blade tip timing and the continuous compressive sensing theory; obtaining the rank constraint optimization problem description based on the atomic norm from the sampling model; obtaining the characteristic matrix through the irregular Vandermonde matrix decomposition; solving the rank constraint optimization problem based on the atomic norm through the alternating projection algorithm; and obtaining the frequency estimation value of the signal from the irregular Vandermonde matrix based on the polynomial root estimation method of the root multiple signal classification. The application is based on the basic principle of the blade tip timing and the continuous compressive sensing theory, and the frequency grid does not need to be divided in advance, so that the sensor layout is not restricted by the sparse linear array layout, and the blade tip timing signal obtained is uniform.

The invention discloses an unmanned aerial vehicle monitoring method based on an extended Kalman filter and a MUSIC algorithm, and the method comprises the steps: a data center processes an echo signal through the MUSIC algorithm, obtains a spectral function, uniformly takes a plurality of angle values in a search window, and substitutes the angle values into a spectral function curve, and the angle value spacing is a step length; finding out a candidate peak in the spectral function curve and matching the candidate peak with the unmanned aerial vehicle, wherein a direction angle of the candidate peak is a measurement azimuth angle of the matched unmanned aerial vehicle; and then, based on the EKF in combination with the measured azimuth angle, obtaining the state vector of the unmanned aerial vehicle, and fitting the flight path of the unmanned aerial vehicle according to the state vectors at multiple moments. The search window and the step length at each moment can be adjusted for spectral function curve fitting at the next moment. According to the search window and the step length dynamically adjusted according to the target state, the accuracy of the result is improved on the premise of controlling the calculation amount; according to the method, a plurality of maximum values in the curve can be processed, and track aliasing is effectively avoided; a result is obtained through EKF, noise influence is reduced, and authenticity is guaranteed.

The invention provides a maritime ship adaptation method based on wireless signal tracking. The maritime ship adaptation method based on wireless signal tracking comprises the following steps: S1, deploying a plurality of groups of RSSI (Received Signal Strength Indicator) sensors, an MIMO (Multiple Input Multiple Output) antenna array and a signal processing unit based on an FPGA (Field Programmable Gate Array) on a maritime operation and maintenance ship; s2, determining the direction of a signal source by using RSSI difference positioning, then determining an accurate azimuth angle and an accurate pitch angle by using a DOA algorithm, finally calculating a final azimuth angle through weighted average of the RSSI azimuth angle and the DOA azimuth angle, and driving a rotating table to adjust antenna beam pointing according to the calculated angle; and S3, executing MUSIC algorithm Kalman filtering in real time by using an FPGA chip, generating interference source motion trail prediction, packaging and uploading trail data, and integrating the trail data to an electronic chart interface of a ship navigation system for visualization.

The application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, wherein each frequency band signal data is obtained through a filter corresponding to a frequency domain of each beam operating frequency band according to multi-beam space data received by the shaped antenna; a spatial spectrum estimation of high resolution is performed on each frequency band signal data by using a MUSIC algorithm to obtain a spatial direction of an incoming wave of an interference signal; adaptive weights of each same-frequency beam are obtained through a two-dimensional joint processing mode of a space domain and a frequency domain based on a preset multi-beam zeroing anti-interference strategy according to the spatial direction of the incoming wave of the interference signal; and the adaptive weights of each same-frequency beam are combined and processed to obtain a comprehensive directional diagram and a multi-beam interference zeroing result, so that the full space domain is covered by beams of different frequency points to realize joint space-frequency suppression of space domain complex interference.

The invention discloses a vortex electromagnetic wave azimuth angle high-resolution estimation method. The azimuth estimation precision under a low signal-to-noise ratio condition is remarkably improved through a characteristic value correction strategy. A uniform circular array (UCA) geometric model based on MIMO is established, vortex electromagnetic waves carrying orbital angular momentum are used as information carriers, and a received signal matrix is constructed through modal domain sampling. Aiming at the problem that the performance of a traditional MUSIC algorithm is reduced under the conditions of a coherent signal source and a low signal-to-noise ratio, a method for carrying out weighted correction on a noise subspace characteristic value is innovatively provided, and the construction of a spatial spectrum function is improved by optimizing the utilization rate of the noise characteristic value. Compared with the traditional MUSIC and FFT methods, the method provided by the invention has more excellent target azimuth estimation performance.

The application discloses a hydrophone array multi-sound source positioning method based on a frost and ice optimization algorithm and belongs to the field of sound source positioning. The method constructs a linear hydrophone array, sets an underwater sound source positioning scene, collects sound source signals and processes the sound source signals to obtain received signals; the received signals are processed by using a MUSIC algorithm, a spatial spectrum is generated to preliminarily estimate the azimuth and the pitch angle and other information of the sound source, the spatial spectrum is normalized, a gravity region and a repulsion region are divided, and a gravity-repulsion type artificial potential field is constructed; a frost and ice RIME optimization algorithm is adopted, an independent sub-population is initialized for each sound source, the position of an individual is updated in combination with the artificial potential field and a frost piercing mechanism in an iterative updating process, and through iterative search, a particle with the highest fitness is selected as a final estimated position. The application overcomes the limitations of a traditional optimization algorithm, significantly improves the precision and reliability of sound source positioning, and is especially suitable for a multi-sound source positioning scene in a complex underwater acoustic environment.