82 results about "Sparse array" patented technology

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.

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.

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 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 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 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 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 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.

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 a communication method, a terminal, network equipment and a storage medium. The communication method comprises the following steps: a terminal sends first information to network equipment, wherein the first information comprises coefficient information; wherein the coefficient information comprises coefficient information corresponding to at least two reference signal resources, or the coefficient information comprises coefficient information corresponding to at least two port groups of the same reference signal resource. According to the invention, the coefficients corresponding to the vectors of the sparse array are reported.

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.

The application discloses a kind of sparse array amplitude-phase error correction-based meshless parameter estimation method, and its implementation steps are: receiving end is laid out sparse array;The received signal of sparse array with array element gain phase error is modeled;Sample covariance matrix is constructed;The output signal of virtual differential common array is calculated by vectorization;Define the atom norm of introducing amplitude-phase error parameter, and construct the optimization model for target direction estimation;By dual atom norm, the original optimization problem is converted into its dual problem;The semi-positive definite programming form of dual problem is deduced;Dual polynomial is constructed, and the wave direction estimation result is obtained by spectral peak search.In addition, the application fully utilizes the virtual differential common array of sparse array to provide extended degrees of freedom and all information, and suppresses the influence of array element amplitude-phase error by defining new atom norm, solves the mesh mismatch problem caused by parameter domain discretization, and obtains high-precision meshless direction estimation 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 discloses a micro-motion exploration frequency dispersion curve extraction method, device and medium, and relates to the technical field of micro-motion exploration, and the method comprises the following steps: S1, calculating the average cross-correlation spectrum of each station pair according to the recorded data of a plurality of stations in an observation array; s2, aiming at the average cross-correlation spectrum of each station pair, identifying a first intersection point frequency of the average cross-correlation spectrum and a frequency axis; s3, based on the first intersection point frequency, performing piecewise fitting on the average cross-correlation spectrum to extract a phase velocity: for a frequency band not greater than the first intersection point frequency, performing fitting by adopting a zero-order Bessel function J0; and for a frequency band of which the frequency is greater than the frequency of the first intersection point, performing fitting by adopting a first-class zero-order Hankel function H0 (1). In a single method, the contradiction of artifact interference in a high frequency band and insufficient detection depth in a low frequency band in the prior art is overcome at the same time, the accuracy and reliability of frequency dispersion curve extraction under the sparse array condition are improved, and the detection depth of micro-motion exploration is effectively expanded.

Provided are a direction of arrival (DOA) estimation method and system for a sparse array based on Vandermonde decomposition reconstruction, relating to the technical field of array signal processing. The method includes: constructing a covariance matrix completion optimization model based on sparse array signals and uniform linear array signals, and performing Vandermonde decomposition by using characteristics of a uniform linear array; introducing a nuclear norm to optimize a rank function in the model, and updating the covariance matrix completion optimization model; and introducing an auxiliary variable to transform the model into a solvable optimization problem, and solving the problem by an alternating direction multiplier method to obtain an optimal estimation value. The DOA is estimated using a root multiple signal classification algorithm.

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.