137 results about "Sparse array" patented technology

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention relates to the technical field of array antennas, and discloses a common-caliber layout-based ultra-wideband array antenna, which comprises a low-frequency unit and a high-frequency unit which are arranged based on a common caliber, and is characterized in that the working frequency of the low-frequency unit covers an fmin-f0 frequency band, and the working frequency of the high-frequency unit covers an f0-fmax frequency band. The low-frequency unit is a broadband dual-polarized antenna, a uniform tight coupling array distributed at equal intervals is formed by adopting a tight coupling principle, and the unit interval is calculated based on a grating lobe suppression condition at the frequency f0. The high-frequency unit is a miniaturized broadband dual-polarized antenna and adopts a sparse array form; the initial unit spacing and the unit number of the sparse array are calculated based on the grating lobe suppression condition at the highest frequency f2 of the f1-f2 frequency band with the side lobe requirement between the f0-fmax frequency bands. The ultra-wideband array antenna gives consideration to full-band array performance, can provide sufficient space for active circuit arrangement, and effectively reduces power consumption and cost.

This invention discloses a deep learning-based channel estimation method for asymmetric all-digital communication systems, comprising: designing an uplink receiving array based on a nested sparse array topology to recover the received signal of the sparse array into a virtual array signal of full dimension; decomposing the obtained virtual signal into a grid part and an off-grid part based on Taylor formula, and designing a two-step-angle estimation net (Ts-AEnet) to estimate the signal angle of arrival; solving a least-squares problem to estimate the signal path gain and reconstructing the downlink channel. This invention proposes an uplink receiving array based on a nested sparse array topology in asymmetric all-digital communication systems, minimizing information loss caused by missing antennas, and proposes a deep learning-based Ts-AEnet angle estimation network, which reduces the complexity of channel estimation while ensuring the system's estimation accuracy.

The invention relates to the technical field of ultrasound, in particular to an ultrasonic phased array sparse arraying method. Comprising the following steps: firstly, constructing a planar rectangular ultrasonic array model and determining basic parameters; setting a fitness function by taking a minimum maximum sidelobe level (MSLL) as an optimization criterion; secondly, introducing a tent chaotic mapping-good point set mixing method to obtain a classical solution population; a quantum bit coding strategy is introduced in the coding stage, individuals in the population are expressed as quantum bits, a standard NOA strategy is executed on the classical solution population to update the population, then the update quantity is synchronized to the quantum bits, and finally a new generation of classical solution population is generated through quantum rotation and collapse operation and the fitness is evaluated; integrating a covariance matrix adaptive evolution strategy (CMA-ES) in the optimization iteration process, and dynamically adjusting the shape, direction and step length of search distribution; and finally updating the global optimum. According to the method, the array element distribution of the sparse array is optimized through the multi-strategy fusion ENOA algorithm, the peak side lobe level of the array can be remarkably reduced, the anti-interference capability and precision of target detection are improved, and meanwhile, the hardware cost of the array is reduced.

The invention discloses an array antenna forming directional diagram synthesis method based on beam space mapping and dimensionality reduction optimization. According to the method, the traditional high-dimensional excitation optimization problem is converted into low-dimensional coefficient optimization by constructing the beam space basis matrix, so that the calculation efficiency of large-scale array antenna shaping directional diagram synthesis is remarkably improved. The specific implementation process comprises the following steps: firstly, establishing a steering vector parameterization model in arbitrary array arrangement, and then constructing a beam space basis matrix containing beams of a forming region and a null region to realize dimension reduction mapping; performing intelligent optimization in a low-dimensional space by adopting an alternating projection algorithm, and synchronously realizing accurate shaping of a main lobe, side lobe level suppression and multi-null control; and finally, obtaining realizable array excitation distribution through beam space inverse mapping reconstruction. Compared with a traditional method, the method can remarkably reduce the dimensionality of the optimization variable, is suitable for any geometric arrangement form such as a conformal array and a sparse array, and can be widely applied to beam optimization design in the fields of 5G communication, phased array radar and the like.

The present application relates to the field of structural damage online monitoring, in particular to a near-field acoustic emission source positioning method based on orthogonal matching pursuit under a sparse array, comprising the following steps: step 1, acoustic emission signal acquisition and preprocessing; step 2, dimension reduction process of the near-field acoustic emission source; step 3, near-field acoustic emission source positioning based on orthogonal matching pursuit; and step 4, near-field acoustic emission source positioning. The present application provides a crack tip positioning method without judging the sensor wave arrival time, which can effectively improve the positioning accuracy.

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.

This document describes techniques and systems for radar systems with an angle determination process for sparse uniform arrays. The described radar systems include a processor and an antenna that can receive electromagnetic energy reflected by an object in a surrounding environment. The antenna includes a one-dimensional (ID) or two-dimensional (2D) sparse array. The processor can use the received electromagnetic energy to determine a signal subspace associated with the object that includes an invariance equation. Using an estimated solution of the invariance equation, the processor determines a solution of the invariance equation. The solution of the invariance equation is used to determine an angular phase associated with the object. The processor can then use the angular phase to determine an angle associated with the object. In this way, the described angle determination process enables a radar system with a sparse array to efficiently determine an angle associated with an object without blind spots.

The invention discloses a V-shaped co-prime array two-dimensional direction finding method and system based on array transformation. According to the method, firstly, array improvement is carried out on the basis of a traditional co-prime array; secondly, receiving a signal through an array antenna of a sparse array structure after array improvement, and obtaining a noise subspace through covariance matrix solving and eigenvalue decomposition; and finally, constructing a MUSIC spectral function, searching to obtain a joint pitch angle and an azimuth angle based on the noise subspace, and finally calculating a DOA estimated value of the information source. The array structures VCACS and VCACSS provided by the invention have a larger degree of freedom and fewer array elements to carry out two-dimensional parameter estimation, so that the azimuth angle and the elevation angle do not influence each other when the azimuth angle and the elevation angle are estimated respectively, and the estimation result is more accurate.

This invention discloses a sparse array optimization method and related equipment based on amplitude mapping. The method includes: acquiring array information and preset sparse constraints of an antenna array; initializing an amplitude weight matrix based on the array information and sparse constraints; mapping the amplitude weight matrix to a sparse binary layout; quantizing the sidelobe level based on the sparse binary layout; if the sidelobe level meets preset requirements, the corresponding sparse binary layout is used as a sparse array; otherwise, the amplitude weight matrix is ​​updated through an optimization algorithm, and the step of mapping the amplitude weight matrix to a sparse binary layout is returned to be executed until the sidelobe level meets the preset requirements. This invention transforms the discrete, massive cell position combination search problem into a continuous, relatively low-dimensional amplitude weight optimization problem, which can greatly compress the solution space and thus efficiently complete the optimization of large-scale arrays. It can be widely applied in the field of data processing technology.

The invention discloses a broadband two-dimensional sparse array layout optimization method based on compressed sensing and subarray optimization, which comprises the following steps of: firstly, optimizing broadband two-dimensional uniform array directional diagram representation based on a two-dimensional subarray optimization method, constructing an optimization model of a sparse two-dimensional array based on L0-norm, setting a two-dimensional subarray constraint condition meeting a beam forming requirement, and constructing a sparse two-dimensional array layout optimization model based on L0-norm; and generating a two-dimensional array coefficient by using the optimization model, carrying out sparsity check on the two-dimensional array coefficient, and finally determining an optimal coefficient according to the generated two-dimensional sparse array to obtain a finally determined two-dimensional sparse array. According to the scheme of the invention, the broadband two-dimensional sparse array arrangement optimization method based on compressed sensing and subarray optimization is provided, and a two-dimensional subarray optimization algorithm and an L0-norm compressed sensing algorithm are combined. According to the method, the array element number of the large-scale two-dimensional array can be efficiently compressed, the beam width, the main-to-auxiliary ratio and the broadband response characteristic of the main lobe of the two-dimensional array can be restrained at the same time, and hardware overhead can be remarkably saved.

The present invention provides a shortwave signal direction-finding method and device for an irregular sparse array, belonging to the technical field of shortwave communication direction-finding. Addressing the direction-finding ambiguity problem in irregular sparse arrays, the present invention employs an array arrangement of antenna elements with different horizontal and vertical positions, simultaneously eliminating angular ambiguity in both azimuth and elevation. A dual-polarized antenna is used to receive incoming signals for direction-finding, fully utilizing the two polarization components of the shortwave skywave signal to improve direction-finding capability. Peak correlation between the current phase difference vector and expected phase difference vectors in different azimuths is used to determine the direction-finding azimuth angle range, reducing the computational complexity of DOA estimation. Azimuth consistency judgment and DOA spectrum peak size comparison methods are used to fuse the direction-finding results to obtain a correct direction-finding result. This method can effectively resolve the direction-finding ambiguity problem in irregular sparse arrays, improve the direction-finding capability for incoming signals with composite polarization patterns, and achieve both accuracy and real-time performance.

The present application relates to the technical field of array signal processing and adaptive beamforming, and particularly relates to a sparse array selection and robust beamforming method under angle perturbation conditions.S1, a candidate array element set is obtained, and a signal model and a common angle perturbation model are established;S2, a common angle perturbation scene sample is generated, and a scene average interference plus noise covariance matrix is calculated based on the scene sample;S3, a scene average sidelobe penalty matrix is constructed in the sidelobe angle domain;S4, a composite robust matrix is constructed, and robust MVDR weight values are solved;S5, a performance index is calculated, and quantile results are counted;S6, a greedy array selection is performed based on the quantile index, and an array selection result is output.The present application improves the low tail reliability of output SINR under perturbation conditions, reduces the probability of accidental failure, suppresses the sidelobe peak outlier risk, improves the sidelobe controllability of endfire / edge angles, realizes the adjustable trade-off of "robust reliability-sidelobe risk", and avoids combination exhaustion.

The application discloses a two-dimensional sparse array single-shot DOA estimation method based on a deep unfolding convolutional network, and belongs to the technical field of cross array signal processing and deep learning. First, a two-dimensional uniform planar array is constructed, and sparse observation data are constructed. Then, an isomorphic tensor mapping mode of separating real parts and imaginary parts is adopted to decompose the sparse observation signals and splice them along the channel dimension, so that a three-dimensional input tensor containing real part channels and imaginary part channels is constructed to maintain the two-dimensional spatial topological structure of the array. Then, the overall structure of the deep unfolding network is unfolded, and a sliding window rule with a predetermined size is used to rearrange the two-dimensional array data into a block Hankel matrix. Finally, network training is performed to realize DOA estimation. The application maintains the two-dimensional spatial structure, improves the reconstruction accuracy, reduces the computational complexity, improves the robustness under a low signal-to-noise ratio condition, realizes high-precision DOA estimation under a single-shot condition, and has good engineering application value.

The present application belongs to the technical field of signal processing, and particularly relates to a sparse array angle estimation method based on Bayesian learning and a device thereof, which comprises the following steps: vectorizing the corresponding received signals received by each array element in a sparse array to obtain output signals in vector form, outputting the output signals in vector form by the sparse array to obtain a two-dimensional output signal model, reducing the dimension of the two-dimensional output signal model to obtain a one-dimensional output signal model; extracting a measurement matrix and a two-dimensional spatial spectrum matrix to be recovered from the obtained one-dimensional output signal model; using a Bayesian learning method to construct an optimization function according to the obtained one-dimensional output signal model and the measurement matrix when the two-dimensional spatial spectrum matrix to be recovered satisfies the RIP characteristic; solving the constructed optimization function to obtain a two-dimensional space-time spectrum, accumulating the obtained two-dimensional space-time spectrum in time to obtain a spatial spectrum of a target, and performing constant false alarm detection on the spatial spectrum of the target to obtain an azimuth estimation of the target.

The invention relates to a target DOA estimation method and system for constructing a differential array based on an MIMO radar sparse array. The method comprises the following steps: subtracting any two array element intervals in an original MIMO sparse array to obtain an array element interval array; forming a differential array; for a non-newly-added first array element interval, if the first array element interval is the same as a certain array element interval in the original MIMO sparse array layout, putting a complex signal corresponding to the array element interval to the position of an array element corresponding to the first array element interval to serve as a first complex signal of a differential array; for the newly added second array element spacing, if only one group exists, obtaining a second complex signal based on two array element spacings in the spacing difference value corresponding to the group; if multiple groups of second complex signals exist, averaging the respective module values and phases of the multiple groups of corresponding second complex signals to obtain third complex signals; a complex signal of the differential array is obtained; and performing DOA estimation to obtain a target angle. According to the invention, the DOA angle measurement precision and accuracy are improved.

The invention provides a mobile sparse array optimization method for low signal-to-noise ratio DOA (direction of arrival) estimation, which is suitable for direction of arrival estimation under the condition of low signal-to-noise ratio. According to the method, array receiving signal data models of the sparse array on a horizontal motion platform and a vertical motion platform are respectively established aiming at virtual array element expansion and degree-of-freedom improvement characteristics formed in the motion process of the sparse array, and a corresponding direction of arrival estimation Ziv-Zakai boundary expression is deduced. Compared with a method using a Cramer-Rao boundary as an array configuration optimization performance index, the Ziv-Zakai boundary can more accurately represent a parameter estimation error lower bound in a low signal-to-noise ratio region, and a theoretical performance lower bound is provided for optimization design of horizontal and vertical motion sparse arrays in the low signal-to-noise ratio region. And ZZB is used as a target function for configuration optimization of the mobile sparse array, so that the sparse array with higher DOA estimation precision in a low signal-to-noise ratio scene is designed.

The invention provides a sparse array underground target size non-contact measurement method based on zebra optimization, and belongs to the technical field of underground target non-contact size measurement. The problem that an existing method cannot effectively suppress the sidelobe level is solved. The method comprises the following steps: constructing a measurement signal pattern optimization model of a sparse array; zebra algorithm parameters are initialized, and parameter configuration of each individual in the population is initialized randomly; updating parameter configuration of individuals in the population according to the zebra foraging behavior; according to the anti-predation behavior of the zebra, when a preset triggering condition is met, performing jump updating on parameter configuration of individuals in the population; the fitness of the current population is calculated, if a termination condition is met, optimal array parameter configuration is output, otherwise, the position of the predator is updated, and then the updating step is returned to continue iteration; and utilizing the optimal array parameter configuration to drive a sparse array to detect the underground target, and calculating the size of the underground target based on an echo signal obtained after detection. The method is mainly used in the nondestructive testing field.

The application discloses a non-circular sparse array DOA estimation performance evaluation method based on information theory, which comprises the following steps: firstly, a multi-dimensional probability density function (PDF) of a received signal is constructed, a joint PDF of the received signal and DOA and a DOA posteriori PDF are derived through information theory, and a system DOA information quantity is obtained by simplifying the DOA posteriori PDF with a Bessel function; then, a DOA posteriori PDF of given noise is derived, and a DOA information approximate upper limit is obtained by simplifying the DOA posteriori PDF with a Taylor expansion; finally, a DOA estimation performance index entropy error is obtained by using the posteriori differential entropy; the application builds a non-circular sparse array system DOA information theory framework based on information theory, in actual signal processing, the entropy error of a parameter can be calculated only by estimating the posteriori PDF of the parameter, and a performance limit independent of an algorithm is provided; in addition, it is found through simulation that the DOA information quantity approaches the DOA information upper limit under a high signal-to-noise ratio, and the entropy error approaches the Cramer-Rao limit, thereby verifying the rationality of the index.

The application discloses a sparse array group array method based on three uniform linear array cascades and application, utilizes three uniform linear array group arrays, reduces mutual coupling influence by setting large subarray element spacing, and can obtain a large-aperture virtual uniform linear antenna array by setting reasonable subarray spacing, and then high-precision DOA estimation without ambiguity can be completed. The application improves the element positions of three subarrays in the existing three uniform linear array cascade, constructs a low mutual coupling array based on three uniform linear array cascades, realizes the optimization target that the element weight values of elements smaller than the subarray element spacing in the difference set are 1, effectively improves the mutual coupling leakage phenomenon existing between the elements of the existing sparse array, guarantees the low mutual coupling, and guarantees the continuous virtual array aperture. Under the condition of strong mutual coupling, the array pattern can obtain the highest DOA estimation precision, meanwhile, with the increase of the mutual coupling strength, the array pattern has good robustness.

A method for designing a sparse MIMO virtual antenna array is disclosed. The method includes: determining the size of an equivalent uniform linear array (ULA) based on a target number of sparse elements of the sparse MIMO virtual antenna array; setting a first optimization target as the maximum signal level of one or more side lobes of the ULA when the beam is directed towards a boresight; setting a second optimization target as the maximum signal level of one or more side lobes of the ULA when the beam is directed towards the edge of the field of view; determining a set of separation distances between antenna array elements of the sparse MIMO virtual antenna array, and simultaneously minimizing the first and second optimization targets simultaneously, or minimizing the sum of the first and second optimization targets; and constructing a sparse MIMO virtual antenna array based on the determined set of separation distances.

The invention relates to a DOA (Direction of Arrival) estimation method based on a triple uniform linear array and a multi-scale attention network, and belongs to a DOA estimation method in the field of array signal processing. Comprising the steps of constructing a triple uniform linear array, performing original data set and preprocessing, constructing and training a multi-scale attention network model, testing the multi-scale attention network model on a test set and performing actual data estimation. The method has the advantages that the regularized and high-degree-of-freedom sparse array design is adopted, the deep coupling multi-scale attention mechanism neural network is combined, the estimation performance of the signal direction is jointly improved, and through verification of analog data and actual data, under the complex conditions of a low signal-to-noise ratio, few snapshots and the like, the method has the advantage that the estimation performance of the signal direction is improved. And high-precision and high-robustness DOA estimation can still be 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 belongs to the technical field of direction of arrival, and relates to a sparse array coherent signal source DOA estimation method based on Toeplitz matrix reconstruction. Comprising the following steps: S1, assuming that a coherent signal source is incident to a sparse array, further constructing a sparse array observation signal model, and further solving a theoretical covariance matrix of the sparse array observation signal model; s2, performing solution operation on the theoretical covariance matrix in the S1; s2, on the basis of the S2, further constructing a Hermitian Toeplitz matrix through virtual array interpolation, and further realizing DOA estimation based on the virtual array interpolation; and S3, obtaining a virtual ULA based on the sparse array of the S1 by combining the S2, performing solution operation on the virtual ULA, and then constructing a Toeplitz matrix based on physical array interpolation so as to realize DOA estimation based on virtual array interpolation. According to the algorithm provided by the invention, the coherent signal source can be accurately estimated under the condition that the degree of freedom and the array aperture are not lost. Simulation experiment results show that the proposed algorithm is superior to the existing algorithm in the aspect of estimating the coherent source.

The invention discloses an indoor positioning method based on two-dimensional sparse array signals. The method comprises the following steps: firstly, deploying a pair of equipment carrying a commercial Wi-Fi network card (Intel 5300) indoors, and configuring and acquiring channel state information (CSI) under multi-time snapshots in a form of'single transmitting antenna + three receiving antennas'; secondly, inhibiting phase distortion in the CSI data by using a linear fitting preprocessing method; thirdly, partitioning the total CSI data according to time snapshots, and inputting the total CSI data into a two-dimensional sparse iteration covariance estimation algorithm to estimate a multipath signal angle-time delay parameter; and finally, through a clustering algorithm, locking direct path features and completing accurate positioning. According to the method, the number of array elements of the receiving array is virtually expanded by using the subcarriers, the bottleneck of limited physical array elements of commercial Wi-Fi equipment is broken through, the positioning precision is greatly improved, the deployment cost is low, the compatibility is high, and the method is suitable for indoor positioning and other scenes.

The invention belongs to the field of array signal processing, and particularly relates to a mobile sparse array optimal configuration method based on a backward greedy pruning strategy. According to the method, on the basis of a direction of arrival estimation performance evaluation objective function Ziv-Zakai boundary, the characteristics of monotonicity and marginal income decreasing about the number of sensor position sets are deduced. And generating an initial uniform array position set meeting aperture constraints under the condition of given sensor number constraints, adopting a backward greedy pruning strategy, gradually eliminating array element positions which contribute the minimum to performance indexes of the Ziv-Zakai boundary in an iteration process until the preset sensor number constraints are met, and outputting an optimal configuration result of the mobile sparse array. According to the method, the calculation complexity of array configuration optimization is effectively reduced while the array estimation performance is ensured, and the method is suitable for mobile sparse array design under the condition of low signal-to-noise ratio.

The invention relates to a communication method, a terminal, network equipment and a storage medium. The communication method comprises the following steps: a terminal receives first configuration information sent by network equipment, wherein the first configuration information is used for determining at least two groups of port information; wherein the port information of different groups corresponds to different reference signal resources, or the port information of different groups corresponds to different port groups of the same reference signal resource. According to the invention, the sparse array can be applied to the communication system, so that the cost and the energy consumption are reduced.

The application discloses a DOA estimation method based on a multi-frequency nested MIMO array, and is based on a traditional nested MIMO array and a low-rank matrix completion theory. First, a set of mutually orthogonal multi-frequency detection waves are sent by a transmitting array of the nested MIMO array, are reflected by a target, are received by a receiving array of the nested MIMO array, are subjected to matched filtering, and then a plurality of groups of received signals are generated. A summation cooperative array with uniformly distributed holes is constructed. Signal reconstruction and low-rank matrix completion operations are performed on the summation cooperative array signals, and finally, DOA estimation is performed. Compared with a traditional DOA estimation method based on a sparse array, the DOA estimation method can improve the maximum estimable incident target number and resolution of the array, and reduce complexity, cost and size. Compared with other DOA estimation methods based on low-rank matrix completion and sparse arrays, the application optimizes the array structure for low-rank matrix completion, and improves the array aperture expansion efficiency.

The invention discloses a fast deconvolution sound source localization method based on array sparsity and broadband comprehensive processing, which comprises the following steps of: selecting microphones to form a sparse array, and converting a regular focusing plane into an irregular plane to obtain grid coordinates; calculating a sparse array cross-spectrum matrix based on coordinates, obtaining a function beam through eigenvalue decomposition and power exponent operation, and outputting the function beam; constructing a frequency domain deconvolution model according to the convolution relationship between the frequency domain and sound source distribution and the convolution relationship between the frequency domain and a rising power point spread function, and solving a sound source intensity distribution matrix by using fast Fourier transform and iterative optimization; estimating a noise floor-to-signal-to-noise ratio matrix, traversing frequency points, taking the maximum signal-to-noise ratio, and fusing the frequency points into a broadband imaging matrix; a time smoothing factor is introduced to carry out weighted fusion on the inter-frame imaging matrix, and positioning is completed; according to the method, low-consumption, high-precision and strong-robustness sound source localization is realized through array rarefaction and fast deconvolution load reduction, precision protection by means of function beam forming and Gaussian filtering, multi-frequency point fusion adaptive imaging, inter-frame smooth flicker suppression and adaptation to complex scenes.