41 results about "Thinned array" patented technology

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 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 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.

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.

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.

The invention discloses a symmetric translation co-prime sparse array design method suitable for a mixed field, and the method comprises the steps: firstly selecting U + 1 antennas, constructing a subarray 1 through sparse uniform linear arrangement at an antenna spacing V, then selecting V-1 antennas, constructing a subarray 2 through right shift after sparse uniform linear arrangement at an antenna spacing U, and finally folding the subarray 1 and the subarray 2, according to the method, the sub-array 3 and the sub-array 4 are constructed, the symmetric translation co-prime array is constructed by integrating the four sub-arrays, and the array can be higher in mutual coupling resistance and higher in parameter estimation precision under the condition that the degree of freedom of the array is equivalent to that of an existing sparse array.

The application relates to a kind of MIMO millimeter wave radar two-dimensional super-resolution angle measurement methods using space-time virtual conversion, comprising: using the orthogonality of transmitting signal, MIMO sparse array is carried out MIMO virtual array transformation, obtain space domain virtual array, obtain the received signal data of space domain virtual array, using the independence of received time domain multiple fast shot data, then carry out time domain virtual transformation, obtain space-time virtual array;Under the antenna layout of space domain virtual array, if the received signal data is single fast shot data, then two-dimensional super-resolution angle measurement is realized using orthogonal matching pursuit (OMP) sparse reconstruction method, if the received signal data is a small amount of fast shot data, then two-dimensional super-resolution angle measurement is realized using sparse bayesian learning (SBL) sparse reconstruction method;If the received signal data is multiple fast shot data, under the antenna layout of space-time virtual array, super-resolution angle measurement is realized using OMP method.The method of the application can increase the effective aperture of array, and significantly improve the angle measurement accuracy of pitch and azimuth dimension.

The invention relates to the technical field of microwave and millimeter wave imaging, in particular to a microwave and millimeter wave sparse array wavenumber domain algorithm combined compressed sensing imaging method and system. According to the technical scheme, the method comprises the following steps: obtaining a backscattering echo signal from a target, and carrying out digital down-conversion processing to obtain a digital IQ demodulation signal; according to the method, the wavenumber domain algorithm and the compressed sensing technology are combined in a cross-dimension mode, efficient focusing and data decoupling are achieved through the wavenumber domain algorithm in the motion scanning dimension, high-quality reconstruction is conducted through the compressed sensing algorithm in the array-distance dimension, and therefore image sidelobes are remarkably restrained, the imaging quality is improved, and meanwhile the image quality is improved. The dimension and calculation complexity of the sensing matrix are greatly reduced, the algorithm efficiency and practicability are improved, and the method can flexibly adapt to various sparse array configurations such as straight lines, broken lines and arc lines.

Disclosed is a method for designing a sparse MIMO virtual antenna array 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 as the maximum signal level of one or more sidelobes of the ULA, when the beam is pointed at a boresight, setting a second optimization target as the maximum signal level of one or more sidelobes of the ULA, when the beam is pointed at an edge of the field of view, determining a set of separation distances among the antenna array elements of the sparse MIMO virtual antenna array, that simultaneously minimises the first and second optimised targets, or minimises a combined sum of the first and second optimisation targets, and building the sparse MIMO virtual antenna array based on the determined set of separation distances.

The invention provides a millimeter wave living body detection radar system. The millimeter wave living body detection radar system comprises a millimeter wave transceiving antenna array, a transceiving front end, a frequency synthesis module, a radar control processing module and a display control computer. According to the invention, the technology of combining millimeter wave radar imaging and Doppler detection is adopted to detect insects carried in a luggage bag according to the detection requirement of the insects carried in the luggage bag; a Ka working frequency band is adopted, and the capability of penetrating materials such as foams, cartons and clothes is achieved; insects are detected by acquiring micro-Doppler information of a target and adopting a technology of combining millimeter-wave radar imaging and Doppler detection; the two-dimensional radar imaging capability and the original data storage capability are realized; a multiple-input multiple-output (MIMO) sparse array is used, compared with a real array, the number of antenna units used by the MIMO sparse array is far smaller than the number of image pixels, and cost and difficulty are reduced. The azimuth optimal resolution is less than or equal to 6mm, and the range resolution is less than or equal to 30mm.

The invention discloses an interference detection method for two-dimensional difference frequency DOA (direction of arrival) estimation, which comprises the following steps of: obtaining a noiseless version of an augmented covariance matrix based on a virtual array by utilizing the structural characteristics of a parallel extended co-prime array through structuring a Toeplitz matrix and calling asymptotic maximum likelihood estimation of a structured covariance matrix. Time samples are integrated into a sample covariance matrix, a MUSIC spectrum of each difference frequency is obtained, the direction of a signal source is searched in a noise subspace, and a unique azimuth angle and a unique pitch angle are correspondingly matched. According to the method, a sparse array structure of a double-parallel extended co-prime array is adopted, a difference frequency Hadamard product principle is utilized, a Toeplitz matrix is structured, a difference frequency-based two-dimensional DOA estimation algorithm is realized, the method is applied to multi-target interference detection, a target function is optimized through mathematical modeling so as to obtain a pitch angle and an azimuth angle of an incident signal, and finally algorithm comparison is performed, so that the interference detection accuracy is improved. And quantitatively analyzing the performance difference between the power normalized spectrum of each method and the RMSE and the operation time, and well verifying the feasibility and the excellent performance.

This invention discloses a distributed laser-induced acoustic communication method and system based on dynamic virtual aperture synthesis, belonging to the field of cross-medium communication technology. The method constructs a distributed self-organizing network using multiple airborne nodes, establishing a unified coordinate system and a global synchronous clock; it senses sea conditions and atmospheric parameters in real time, plans the equivalent emission aperture size as needed, and uses a sparse array algorithm to select nodes to form a dynamic virtual emission array; it calculates the cross-medium propagation phase compensation amount by combining node position deviations and the influence of sea surface waves; and it achieves precise coordinated emission of laser pulses by nodes relying on the global clock, synthesizing highly directional acoustic signals in underwater target areas through photoacoustic effects. This invention overcomes the limitations of single-platform physical aperture and energy, utilizes distributed collaboration to achieve large-aperture virtual synthesis, effectively improving laser-induced acoustic communication distance, signal-to-noise ratio, and anti-interference capability, and possesses good network self-healing and task adaptability.

The application discloses a kind of sparse array design method based on coprime array redundancy analysis, it is related to DOA estimation field.The steps include: step 1: the signal model of coprime array is established;Step 2: coprime array is analyzed to redundancy virtual element;Step 3: based on the analysis result of step 2, two shift coprime arrays are formed by moving subarray, which are CATrS-I array structure and CATrS-II array structure respectively;Step 4: using CATrS-I array structure or CATrS-II array structure carries out DOA estimation.The application gives two kinds of subarray shift coprime arrays CATrS-I and CATrS-II, can effectively increase degree of freedom and continuous degree of freedom under the condition of keeping the total number of physical elements unchanged, and suppresses element mutual coupling.

Disclosed methods and apparatuses embody techniques for advantageous packing and deployment of an inflatable net for use as a randomized sparse array offering gain and beam-count equivalent with a conventional phased-array of the same number of antenna elements and near-equivalent spatial discrimination (beam definition) as compared with a conventional phased array of the same effective diameter. Operation of the deployed net as a randomized sparse array greatly relaxes the mechanical tolerances associated with deployment, such that array performance does not depend on achieving precise or predetermined spatial relationships among or between elements in the array. Additionally, the voids inherent in the net structure offer greatly reduced overall mass and volume, making practical the stowage and launch of inflatable nets that deploy into very large arrays.

The application discloses a signal processing method, a sidelobe suppression method and system for a sparse array antenna, splits a main subarray into multiple smaller subarrays, respectively performs beam forming spectrum calculation, performs normalization processing on the spectra, and reconstructs a beam forming spectrum by taking a minimum value at each angle, in the embodiment of the application, by using the characteristics that different subarrays are sensitive to different degrees of sidelobes at different angles, a lower relative sidelobe level is realized on a new angle spectrum, and the sidelobe level is effectively suppressed; meanwhile, each subarray retains the first element and the last element of the corresponding main subarray, the high level of the main lobe and the consistency of the antenna aperture are maintained, and therefore the angle resolution is unchanged.

The invention discloses a sparse array virtualization and degree-of-freedom improvement method based on polynomial interpolation. Constructing a virtual array according to a mathematical relationship between the number of physical array elements and a virtual aperture, and further determining a sparse array; receiving the signal by adopting each physical array element in the sparse array and processing to obtain a baseband signal; estimating a covariance matrix of the received signal according to the baseband signal, and constructing a virtual array containing holes; estimating the baseband signal by adopting a polynomial interpolation algorithm to obtain a virtual array element signal at the hole, and further forming a complete virtual array; and obtaining a full-rank covariance matrix for the complete virtual array by adopting a covariance matrix reconstruction method, and performing angle estimation on the full-rank covariance matrix by adopting a DOA (Direction of Arrival) estimation algorithm to obtain incident angle information of the target. According to the method, after the polynomial interpolation is calculated in advance, the calculation complexity is close to that of a traditional uniform linear array, received information can be utilized in a low-complexity mode, and target angle estimation with higher performance is achieved.

The application provides a common excitation amplitude sparse reconfigurable array synthesis method and relates to information transmission and processing technology.The application comprises the following steps: 1) constructing a mathematical optimization model by using the radiation characteristic requirements and the nulling requirements of a beam to be synthesized, and solving the model by using the particularity of conjugate symmetric excitation and a continuous convex approximation algorithm; and 2) introducing a slack variable to help solve the feasible point by using a feasible point tracking algorithm, and overcoming the infeasible solution caused by the non-convex constraint.The application has the advantages that a sparse array can form multiple radiation direction patterns, and meet multiple different application scenarios; the number of array elements is reduced by designing the sparse array, so that the system cost is further reduced; the characteristic of conjugate symmetric excitation weight reduces the optimization variables, so that the application has the advantage on a large array; and 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.