70 results about "Thinned 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.

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 millimeter wave near-field imaging, in particular to a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, and the method comprises the steps: dividing a complete comprehensive array of the SAR system into a plurality of sub-arrays, selecting local echo data corresponding to the plurality of sub-arrays from complete echo data, and calculating the local echo data of each first-stage sub-array in (u, v, v); according to the method, a uniform sampling grid with a uniform sampling rate in an (x, y, z) coordinate system is obtained, the uniform sampling grid is converted into a first-level non-uniform sampling grid in the (x, y, z) coordinate system, a first-level sub-image is reconstructed in the first-level non-uniform sampling grid based on local echo data, and a pairwise coherent superposition process is iteratively executed; and obtaining a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system. According to the invention, the calculation complexity of imaging can be effectively reduced, and accurate and efficient near-field three-dimensional imaging of an arbitrary linear sparse MIMO array cylindrical scanning SAR system is realized.

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.

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 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 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.

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 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 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 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.

A DOA estimation method of a delayed co-prime sparse linear array is suitable for signal processing of DOA estimation of radar, sonar and wireless communication, and comprises the following steps: measuring a signal source by using the delayed co-prime sparse array to obtain a signal sample; constructing two uniform subarray segmentation schemes with different delays, and segmenting the signal sample to obtain data; analyzing the segmented data by using a unitary ESPRIT algorithm to obtain a winding phase; and constructing a congruence equation set for each pair of wound phases, and solving the congruence equation set by using a robust Chinese remainder theorem algorithm to obtain a DOA estimated value.

The invention relates to the technical field of radar three-dimensional imaging, in particular to a TDM-MIMO radar signal processing method based on an improved sparse array and related equipment. The TDM-MIMO radar signal processing method based on the improved sparse array comprises the following steps: acquiring first echo data of a moving target based on a first MIMO array; exchanging the positions of a transmitting channel and a receiving channel in the first MIMO array to obtain a second MIMO array, and determining an equivalent reference channel from the second MIMO array; determining a delay increase rate based on the equivalent reference channel; wherein the time delay increase rate represents the increase rate of the echo time delay generated by the moving target due to displacement in the process of switching the adjacent communication channels; and correcting the first echo data based on the delay increase rate.

The invention particularly relates to a sparse array DOA (direction of arrival) estimation method based on an optimized array manifold dictionary, and the method comprises the steps: firstly carrying out the layout optimization of an initial uniform area array through combining a genetic algorithm, constructing an eight-element sparse array structure, and maintaining a directional diagram which is close to a full array while greatly reducing the number of array elements; performing energy screening on multichannel observation signals acquired by the sparse array, and extracting a dominant frequency band to improve an effective signal-to-noise ratio; and an optimized array manifold dictionary is constructed based on a phase compensation mechanism and an amplitude weighting mechanism, and the internal correlation of the dictionary is reduced by minimizing off-diagonal elements of a dictionary Gramer matrix. And on the basis, sparse reconstruction is carried out on the optimized dictionary by using an orthogonal matching pursuit algorithm, so that a direction-of-arrival estimation result of the sound source signal is obtained. According to the scheme, inherent direction finding fuzziness and grating lobes of the sparse array are effectively suppressed, the direction estimation precision is improved, and high reliability and high robustness are still kept under the condition that the number of array elements is remarkably reduced.

The application discloses a V-shaped coprime array two-dimensional direction finding method and system based on array transformation, which first improves the array on the basis of a traditional shape coprime array. Secondly, the array antenna of the array improved shape sparse array structure receives signals, and through solving a covariance matrix and eigenvalue decomposition, a noise subspace is obtained. Finally, a MUSIC spectrum function is constructed, joint elevation and azimuth are searched and obtained based on the noise subspace, and finally, the DOA estimation value of the signal source is calculated. The array structure VCACS and VCACSS proposed in the application have greater degrees of freedom and fewer array elements for two-dimensional parameter estimation, so that the azimuth and the elevation are not affected when the azimuth and the elevation are estimated respectively, and the estimation result is more accurate.

A method for designing a sparse MIMO virtual antenna array is disclosed. The method includes determining a 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 to a maximum signal level of one or more sidelobes of the ULA when a beam is pointed to a boresight, setting a second optimization target to a maximum signal level of the one or more sidelobes of the ULA when the beam is pointed to an edge of a field of view, determining a set of spacing distances between antenna array elements of the sparse MIMO virtual antenna array that simultaneously minimizes the first optimization target and the second optimization target or minimizes a sum of the first optimization target and the second optimization target, and constructing the sparse MIMO virtual antenna array based on the determined set of spacing distances.

This application proposes a near-field three-dimensional imaging method for sparse MIMO arrays based on efficient spectrum reconstruction. The method includes: establishing a two-dimensional MIMO array imaging model, assuming that the receiving antennas of the two-dimensional MIMO array are fully arranged, and analyzing the imaging process of a single transmitter; further assuming that the receiving antennas of the two-dimensional MIMO array are sparsely arranged along the X-axis, and determining the actual recoverable spatial scattering signal based on the imaging process of a single transmitter; performing a Fourier transform on the actual recoverable spatial scattering signal first along the Y-axis and then along the X-axis to obtain a uniform wavenumber spectrum; performing a two-dimensional inverse Fourier transform on the uniform wavenumber spectrum to obtain the target spatial scattering signal, and then converting it to the frequency domain; extracting the component corresponding to the frequency of the target distance in the frequency domain, and performing phase compensation to recover the sub-image corresponding to a single transmitter; and finally accumulating the sub-images corresponding to all transmitters to obtain a complete three-dimensional imaging result.

This invention proposes a direction-of-arrival (DOA) estimation method based on a sparse circular array using the golden ratio. The method defines the antenna position and three-dimensional coordinates using the golden ratio, constructing a sparse array. A DOA vector is established based on the azimuth and elevation angles, and the path difference and phase difference are calculated. These are then combined with multi-shot data to form a covariance matrix. To improve robustness under low snapshot and low signal-to-noise ratio conditions, adaptive diagonal loading is employed to adjust the noise eigenvalue distribution and enhance the distinguishability between the signal and noise subspaces. Addressing the issues of traditional MUSIC algorithms' accuracy depending on step size and poor real-time performance, a three-step search is designed: a coarse search to locate local maxima, a ternary trigonometric iteration to narrow the interval, and a fine search followed by quadratic surface fitting to eliminate grid errors. This method effectively suppresses sparse array grating lobes, balancing high accuracy and real-time performance.

This application proposes a high-resolution imaging method, apparatus, and storage medium for a planar microphone array in the differential domain. The method is applied to typical planar sparse array structures such as two-dimensional nested arrays. By vectorizing the covariance matrix of the received signal, the differential comatrix of the original array is obtained. Acoustic imaging is then performed based on this differential comatrix, resulting in a uniform rectangular array with higher degrees of freedom in the differential domain. This allows for high-resolution acoustic imaging with a small number of microphones. Numerical simulation experiments show that, with the same number of microphones, the acoustic imaging localization algorithm based on the differential domain of the nested planar array achieves higher resolution than existing acoustic imaging algorithms that directly locate typical planar arrays.

An MIMO array sparse rapid design method of a fuzzy function generator based on deep learning comprises the following steps: firstly, directly learning a fuzzy function mapping relation corresponding to virtual array configuration and an incident angle by using a deep learning model so as to replace a traditional analysis fuzzy function calculation process depending on a manifold matrix; and then performing efficient performance evaluation on the candidate sparse array based on the fuzzy function generator, and obtaining a sparse array structure meeting the requirements of high resolution and low sidelobe in combination with an optimization strategy. By means of the technology, acceleration of more than 35 times can be achieved on the premise of not explicitly calculating the fuzzy function, the fuzzy function prediction root mean square error (RMSE) lower than 0.1 is kept, and therefore the sparse array design time is greatly shortened. Experiments on a 2T4R K-band MIMO radar prove that the designed array achieves 10 dB sidelobe level in a 100-degree field of view, the angular resolution is improved by about 32.8%, and the effectiveness and engineering application value of the method in high-resolution MIMO sparse array design are proved.

The invention relates to the technical field of antennas and microwaves, in particular to a low-sidelobe sparse array antenna control method based on step amplitude and a phased array radar. The method comprises the following steps: calculating normalized low-sidelobe amplitude weights of antenna units in a pitching direction and an azimuth direction according to design indexes, setting an amplitude maximum value for each row of antenna units, setting an amplitude value of each antenna unit, calculating a probability vector, repeatedly generating a distribution vector of each row of antenna units, selecting a result with optimal performance, and calculating a distribution vector of each row of antenna units according to the distribution vector. And taking as a final result. According to the invention, the sidelobe level of the array can be significantly suppressed under the condition of low amplitude modulation degree of freedom.

A system and method is presented for determining a plurality of MIMO virtual antenna arrays. Each virtual antenna array of the plurality of MIMO virtual antenna arrays is associated with a subset of a plurality of transmit antennas and a subset of a plurality of receive antennas. Signals received by the plurality of receive antennas are used to determine signal spectrums for each MIMO virtual antenna array of the plurality of MIMO virtual antenna arrays. Each signal spectrum includes signal magnitude values and has associated angle values, wherein main lobe positions of the signal spectrums of the plurality of MIMO virtual antenna arrays are at a same angle across all signal spectrums and sidelobe positions of the signal spectrums are not at the same angle across all signal spectrums. A first combined MIMO virtual antenna array signal spectrum is determined by combining minimum values of the signal spectrums at each angle value. The first combined MIMO virtual antenna array signal spectrum is used to detect an object.

The application provides a broadband near-field beam training method, device and medium, and belongs to the technical field of Internet communication transmission. The method comprises the following steps: acquiring a system model of a broadband super-large array communication system; determining a first beam splitting effect of a sparse array in the broadband in a spatial domain and a second beam splitting effect of the broadband in a frequency domain based on the system model; and performing super-resolution broadband near-field beam training based on the first beam splitting effect and the second beam splitting effect. The technical scheme can realize super-resolution broadband beam training with extremely low overhead.

The invention provides a common excitation amplitude sparse reconfigurable array synthesis method, and relates to an information transmission and processing technology. The method comprises the following steps: 1) constructing a mathematical optimization model through a radiation characteristic requirement and a null requirement of a to-be-synthesized wave beam, and solving the mathematical optimization model by using particularity of conjugate symmetric excitation and a continuous convex approximation algorithm; 2) utilizing a feasible point tracking algorithm, introducing a slack variable to help to solve a feasible point, and overcoming a non-feasible solution caused by non-convex constraint; the method has the advantages that a plurality of radiation directional diagrams can be formed by one sparse array, so that a plurality of different application scenes can be met; the number of array elements is reduced through the design of a sparse array, so that the system cost is further reduced; due to the characteristic of excitation weight conjugate symmetry, optimization variables are remarkably reduced, and the advantages of the method on a large-scale array are highlighted; the DRR constraint is added, so that the dynamic range and the quantization precision required by the attenuator are reduced, and the hardware complexity is simplified.

This invention discloses a high-degree-of-freedom, low-coupling sparse array design method based on fourth-order cumulants, comprising: A: constructing a non-Gaussian signal receiving model and constructing a fourth-order differential array of the non-Gaussian signal using fourth-order cumulants; B: constructing a coprime displacement array composed of two coprime displacement subarrays and determining the array parameters; C: constructing an expanded nested array composed of two nested subarrays and determining the array parameters; D: constructing an expanded nested coprime displacement array based on the coprime displacement array and the expanded nested array and determining the expansion factor, ultimately obtaining a high-degree-of-freedom, low-coupling sparse array based on fourth-order cumulants. This invention can achieve stronger anti-coupling capability while maintaining a degree of freedom comparable to existing sparse arrays, and can significantly improve the signal estimation accuracy and direction-of-arrival resolution.