397 results about "Array element" patented technology

The invention provides an acoustic array adaptive calibration correction system applied to an underwater moving target, which relates to the technical field of marine equipment and comprises a multi-source excitation and environment perception module, an intelligent array perception and diagnosis module, an adaptive position inversion and uncertainty quantification module and a closed-loop calibration execution and self-learning optimization module. The multi-source excitation and environment perception module is responsible for providing reference signals required by calibration and establishing a correlation model of environment and formation distortion, and the intelligent array perception and diagnosis module realizes multi-modal data acquisition, array element health state monitoring and formation geometry self-perception. The adaptive position inversion and uncertainty quantization module completes signal processing, position estimation and error quantization propagation, and the closed-loop calibration execution and self-learning optimization module executes a compensation strategy, verifies a calibration effect and continuously optimizes system performance through online learning; the system solves the problem that a traditional method cannot process environment time varying, multi-sensor conflicts and uncertainty quantization.

The invention relates to an ultrasonic phased array full-focusing imaging method for acoustic wave multi-path propagation compensation of a layered composite material, which is characterized by comprising the following steps of: arranging an ultrasonic phased array probe with N array elements on a composite material piece to be detected, obtaining complete echo data by taking each array element as a transmitting array element and taking all array elements as receiving array elements in sequence in a full-matrix acquisition mode; a two-dimensional pixel grid is established in an imaging area, a Monte-Carlo perturbation mechanism used for representing the acoustic wave propagation uncertainty in the layered composite material is introduced into a propagation model for each emission array element-receiving array element-pixel point combination, and the acoustic beam emission direction, the propagation path and the equivalent acoustic velocity are subjected to random perturbation, so that the acoustic wave propagation uncertainty in the layered composite material can be represented. Generating a plurality of equivalent sound wave propagation paths, calculating two-way propagation time of each path, and performing weighted statistics on energy contributions of different paths according to array element sound beam directivity to obtain equivalent propagation time delays of pixel points; and performing delay correction on a full-matrix echo signal by using the equivalent propagation time delay, and performing coherent superposition on signals of all transmitting-receiving array element combinations to realize dynamic focusing of a full-pixel grid, and finally reconstructing an ultrasonic full-focusing imaging image with high resolution and high signal-to-noise ratio. The time delay error caused by propagation path deviation and sound velocity non-uniformity in the layered composite material can be effectively compensated, the focusing precision and spatial resolution of full-focusing imaging are improved, meanwhile, an existing TFM imaging frame does not need to be changed, and the method has good engineering implementability and popularization and application value.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

The invention discloses a GNSS anti-interference closed-loop phase self-correction method based on deep learning, and relates to the technical field of satellite navigation and artificial intelligence, and the method comprises the steps: obtaining an anti-interference array output signal and weight information through an array antenna and space-time adaptive processing; extracting a carrier-to-noise ratio, a weight phase feature and historical positioning quality, and constructing a deep learning feature vector; reasoning an array element-level phase correction reference quantity by using a multi-layer perceptron model, and realizing accurate phase compensation of an array output signal by combining phase error layered modeling, quick-change disturbance prediction and weight sensitivity estimation; an anti-interference perception observation covariance dynamic scaling mechanism is introduced into RTK positioning calculation; on-line learning and model updating are triggered through a positioning quality evaluation result, so that the system has an adaptive evolution capability. According to the method, the GNSS carrier phase quality and the positioning precision under the strong interference condition can be remarkably improved, deep fusion of anti-interference processing and high-precision positioning is realized, and the method has important engineering application value.

The invention relates to the field of underwater sound signal processing, in particular to an underwater DOA (direction of arrival) estimation method based on graph nerves and a convolutional neural network, which comprises the following steps: 1, establishing a linear array, and enabling narrow-band signals to simultaneously reach an underwater sound array; 2, performing signal preprocessing to obtain a signal covariance matrix, and performing normalization processing; 3, extracting correlation between array elements and spatial features of array signals, and performing data supplementation on sparse linear array information; 4, forming a double-branch structure, enhancing the information aggregation capability, and extracting features from a space path and a time domain path; and 5, constructing an adjacent matrix, filling node features of damaged array elements, adopting a double-branch structure, extracting spatial features and time domain features, carrying out feature integration, and outputting a DOA estimation result. The spatial correlation between array elements is extracted and the array sparsity problem is processed by using the graph neural network, and the time domain features of the signals are extracted in combination with the convolutional neural network, so that more accurate and more robust DOA estimation can be realized under the conditions of low signal-to-noise ratio and array sparsity.

The invention relates to the technical field of array signal processing, in particular to a space-frequency power inversion anti-interference method fusing SMI-LDL decomposition. The problems that an existing space-time anti-interference technology is high in calculation complexity, and real-time processing is difficult to achieve in limited resources of a navigation receiver are mainly solved. The method comprises the following steps: firstly, converting a radio frequency signal received by an array into a digital intermediate frequency signal and carrying out sliding window partitioning processing; windowing the data block and performing FFT (Fast Fourier Transform) to obtain a frequency domain array; then calculating an auto-covariance matrix R of other array elements except the reference array element and a cross-covariance matrix r of the reference array element and other array elements; lDL decomposition is carried out on the auto-covariance matrix to obtain a lower triangular matrix L and a diagonal matrix D, and inversion is carried out on the lower triangular matrix L and the diagonal matrix D respectively, so that huge operand caused by direct inversion of the covariance matrix is avoided; and finally, calculating an optimal weighting vector according to a power inversion criterion, and performing spatial filtering on the frequency domain data.

The invention belongs to the technical field of crossing of information and ocean science and technology, and discloses a UUV broadside parallel array covariance weighted fusion DOA estimation method. The method comprises the following steps: firstly, calculating auto-covariance and cross-covariance matrixes of two sub-arrays, carrying out weighted fusion on covariance matrixes of different sources through differential common-array expansion of a virtual aperture, then reconstructing a complete covariance matrix by respectively adopting a convex optimization method of trace regularization and nuclear norm regularization according to the characteristics of the fused matrix, and finally, carrying out weighted fusion on the covariance matrixes of different sources. The pitch angle and the azimuth angle of the target are jointly estimated by performing subspace decomposition on the constructed extension matrix, and the effectiveness of the method is verified by a simulation result. According to the method, the precision of parameter estimation is improved through data fusion, the degree of freedom is remarkably improved under a small number of physical array elements, high-precision azimuth estimation of the UUV on multiple targets in the underwater environment is achieved, the target detection capacity of the UUV is enhanced, and the method has high engineering application value.

The invention discloses a centralized phased array beam control method and system based on an FPGA, and relates to the technical field of phased array beam control, and the method comprises the steps: carrying out the real-time iterative operation through an incremental adaptive correction kernel in a centralized control FPGA, and obtaining an amplitude-phase correction increment and a corresponding target effective frame number; performing increment differential coding on the amplitude-phase correction increment and the corresponding target effective frame number in a centralized control FPGA (Field Programmable Gate Array) to generate an increment differential beam control message; sending the increment difference beam control message to an array element FPGA (Field Programmable Gate Array) at the front end of an array, executing frame-level atom switching according to a target effective frame number, and fusing the amplitude-phase correction increment and a locally stored old beam control weight in the switching process to obtain a final array element beam control weight; generating a real-time beam control directional diagram based on the final array element beam control weight; the stability and convergence precision of beam control are improved, and the bandwidth requirement of a control link is reduced.

A photonic integrated circuit (PIC) is disclosed herein. The PIC can include a substrate, a main optical waveguide supported by the substrate. The main optical waveguide can be in communication with an electromagnetic radiation source, and configured to receive electromagnetic radiation from the electromagnetic radiation source. A first branch optical waveguide can be optically coupled to the main optical waveguide at a first location. An optical phased array (OPA) can include plurality of array elements, each having an optical antenna and an optical phase modulator. At least some array elements within a first subset of the plurality of array elements can be optically coupled to the first branch optical waveguide wherein locations of at least some of the plurality of array elements are aperiodic in one or more directions on the substrate.

The invention relates to the technical field of nondestructive testing, and discloses a wind power casting nondestructive testing method and system, and the method comprises the steps: obtaining three-dimensional surface point cloud data of a to-be-tested wind power casting, employing a sound ray tracking algorithm to calculate a sound path distance and a propagation angle between each imaging point and each array element in an imaging grid, and generating a focusing rule; performing full-matrix data acquisition to obtain an original time domain signal, performing wavelet packet decomposition on the original time domain signal, and determining the peak kurtosis of the original time domain signal; carrying out weighted coherent superposition on any imaging point in the imaging grid based on a focusing rule and an original time domain signal, and reconstructing an image; and identifying a defect indication area in the reconstructed image, calculating an average spatial frequency gradient, determining the number of iterations of a deconvolution algorithm, performing deconvolution processing on the image of the defect indication area, and determining the contour and size of the defect. According to the method, the defect signal can be effectively enhanced, the noise interference is inhibited, and the accuracy of the defect detection result of the wind power casting is improved.

The embodiment of the invention provides a laser scanning method and device, laser communication equipment and a laser communication system. The laser scanning method comprises the steps that a pointing center point where a target object is located is determined according to ephemeris information of the target object; the rotation angles of a plurality of array elements in the transceiving array are controlled, so that light beams emitted by the array elements are subjected to incoherent superposition to form scanning light spots, and the light beams emitted by the array elements are obtained through deflection of incident light of the transceiving array; scanning in a preset range pointing to the center point by adopting the scanning light spot, and controlling the rotation angles of a plurality of array elements in the transceiving array to enable light beams emitted by the plurality of array elements to be coherently superposed to form a scanning light beam under the condition that the first position of the target object is scanned; and scanning the first position with the scanning beam to capture the target object. The non-coherently stacked scanning light spots are firstly used for coarse positioning, and then the coherently stacked scanning light beams are used for fine positioning, so that efficient, rapid and accurate capturing of the rapidly moving target is realized.

The electronic equipment provided by the invention comprises a frame and an antenna assembly, and the frame comprises a first edge and a second edge which are connected in a bending manner; the antenna assembly comprises a first radiator, a second radiator and a signal source, and the first radiator is arranged on the first side of the frame; the second radiator is arranged on the second side of the frame; the signal source is configured to be electrically connected with the first radiating body and the second radiating body in the first working mode so as to excite the first radiating body and the second radiating body to receive or emit a satellite communication frequency band at the same time, so that the first radiating body and the second radiating body form an array element arranged on the intersection side of the electronic equipment, and the antenna assembly is an array antenna; the formation of crossed or orthogonal radiation energy is facilitated, and the circular polarization performance of satellite communication or navigation of the electronic equipment is improved.

The invention discloses a multi-antenna anti-interference method, multi-antenna anti-interference equipment, a storage medium and a program product, and aims to solve the contradiction between poor performance of a sub-array architecture and overhigh calculation complexity of a full-array architecture. The method comprises the following steps: carrying out global synchronization processing by taking all array elements as a whole, and determining a shared synchronization point; the array is divided into a plurality of sub-arrays, each sub-array uses the synchronization point and independently calculates a set of anti-interference weights, and accordingly, sub-array output signals subjected to interference suppression are generated; and determining a group of optimal merging coefficients through an optimization algorithm with the purpose of maximizing the final signal quality, and performing weighted merging on all sub-array output signals to generate a final output signal. In addition, the method also comprises a mechanism for periodically updating an anti-interference weight and a merging coefficient. According to the method, through step-by-step processing and optimal combination, the calculation complexity is effectively reduced, meanwhile, the performance loss of a traditional sub-array scheme is avoided, and the anti-interference performance of a system in a dynamic complex environment is improved.

The invention provides a surface crack defect identification method and device based on machine learning, and relates to the technical field, and the method comprises the following steps: carrying out geometric scanning on a workpiece curved surface through an array type alternating current electromagnetic field sensor, and reconstructing a probe geodesic track in combination with an inertial measurement unit and a photoelectric encoder; a curved surface parameter field and an array element pose table are generated through fitting, and collected three-axis magnetic induction intensity vectors are converted into local components aligned with the crack length direction and the crack depth direction through direction cosine projection; the processed signals are demodulated and subjected to speed correlation correction to obtain a multi-frequency amplitude-phase feature cluster, and a physical consistency feature vector and geometric priori are generated by combining a curved surface forward model and a three-dimensional grid response library. And a machine learning model is obtained through joint loss training, butterfly diagram interpretation and pseudo defect decoupling are introduced in an inference stage to realize secondary check, and a crack existence label and a crack geometric parameter set are output. According to the method, the precision of identifying the surface crack of the large-curvature metal object can be improved.

The invention relates to a satellite mobile communication antenna adaptive beam prediction and control method, which comprises the following steps: acquiring motion data of a user terminal, signal state data of a satellite antenna receiving end, satellite-ground attitude data of a satellite and environment interference data of the ground, and processing to obtain a multi-source data set; the method comprises the following steps: constructing a geometric correlation feature, a gain correlation feature, a sidelobe correlation feature and an array element phase correlation feature of a multi-source data set and beam parameters, constructing a correlation matrix, and splicing the correlation matrix with the multi-source data set to obtain a complete input feature set; a beam prediction model is constructed, and training and verification are carried out; and converting the output of the beam prediction model into a control signal to drive an antenna array element to adjust, correcting model parameters based on the link signal-to-noise ratio, the bit error rate and the throughput collected by the user terminal, and splitting / combining the beam. According to the method, the beam parameter prediction precision and environment adaptability can be improved, interference is suppressed, resource allocation is optimized, and stability and high efficiency of satellite communication in a high-dynamic complex environment are guaranteed.

The present application relates to the technical field of radar signal processing, aiming at the target positioning and imaging ambiguity caused by the distance-angle coupling of the traditional frequency diversity array beam pattern, and the problem that more array elements are needed for random frequency offset to remove ambiguity, a FDA structure based on joint design of intra-array frequency offset and inter-array frequency offset is proposed, the transmission energy is focused on the expected distance-angle area to realize distance-angle decoupling, and by increasing the number of intra-array frequency increments, the number of array elements is increased to improve the target ambiguity-free limit, the number of frequency increments is increased from N of the traditional FDA to, the order of magnitude of the ambiguity-free limit can be greatly increased, and the tolerance of the target ambiguity-free limit is greater. By optimizing the parameters of the array element and inter-array frequency offset design, the de-ambiguous imaging can be realized without complex imaging algorithm.

The application discloses a near-field strong interference suppression method based on spherical wave deconvolution beam forming positioning, which comprises the following steps: performing fine grid division on a near-field scanning area of a linear array; performing spherical wave focusing beam forming on the near-field scanning area and two-dimensional deconvolution beam forming about distance-angle under the condition of unknown sound source position; calculating the distribution entropy characteristics of the deconvolution beam forming result in the angle dimension, and using a preset threshold to correlate the interference positioning of a target; constructing an array flow pattern matrix A of each near-field target l and generating a covariance matrix A; calculating the orthogonal complement space projection matrix of the covariance matrix A, and applying the orthogonal complement space projection matrix to the array element domain covariance matrix C to perform near-field interference suppression; using a far-field plane wave model to perform deconvolution beam forming calculation on the covariance matrix for suppressing near-field interference, so as to obtain passive detection results of the near-field strong interference suppression. The application can realize near-field interference positioning and interference suppression under the condition that the interference position is unknown.

The invention discloses a broadband high-resolution robust beam forming method and device based on space-frequency mutual coupling domain phase difference compensation, equipment and a storage medium, and the method comprises the steps: converting an underwater time domain receiving signal to a space-frequency mutual coupling domain according to a preset frequency domain conversion algorithm, determining an expected airspace phase difference corresponding to a preset reference frequency and phase compensation information corresponding to the initial underwater receiving signal according to the preset reference frequency, the sound velocity of the medium in water and the distance between adjacent array elements; performing phase correction on the initial underwater received signal; determining an optimal underwater receiving beam weight vector according to a preset optimal receiving beam weight limitation formula, the standard covariance matrix and the steering vector uncertainty set; the underwater broadband high-resolution robust beam is formed based on the received signal strength of each preset scanning direction, so that the anti-interference capability of the underwater broadband high-resolution robust beam and the accuracy of an underwater orientation result can be improved, and reliable detection and high-precision orientation of a broadband signal target in a complex underwater sound environment can be realized.

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 monostatic symmetric nested array MIMO system and a near-field positioning method, belonging to the field of monostatic MIMO near-field positioning technology. This invention can effectively improve the positioning performance during near-field estimation. The monostatic symmetric nested array MIMO system proposed in this invention has a symmetrically distributed transmit and receive arrays. Compared to other symmetric MIMO systems, its differential array is a positionally continuous array with more degrees of freedom. With the same physical array aperture, fewer physical array elements are actually used, reducing costs. The near-field positioning method proposed in this invention calculates the fourth-order statistics of the signal and quantizes them to obtain a virtual differential array of the differential array, achieving accurate near-field positioning of sparse MIMO structure arrays. Furthermore, it can be extended to more sparse MIMO symmetric systems.

This application relates to the field of medical ultrasound measurement technology, specifically providing a method for in-situ measurement of skull thickness and sound velocity using ultrasound. The method includes: acquiring reflected echo data from N transmitting array elements, wherein the N transmitting array elements sequentially transmit ultrasound waves to the location to be measured on the skull, and array elements centrally symmetrical to the transmitting array elements receive the reflected echoes of the ultrasound waves; arranging the reflected echo data from the N transmitting array elements together to form a wave train diagram of the N transmitting array elements; extracting the sound pressure amplitude values ​​corresponding to the normal incident reflection time from the wave train diagram, and superimposing the extracted sound pressure amplitude values ​​to form a superimposed amplitude spectrum, with the maximum superimposed sound pressure amplitude value corresponding to the wave train diagram being the equivalent matching velocity; and calculating the sound velocity and thickness at the location to be measured on the skull based on the equivalent matching velocities at the maximum superimposed sound pressure amplitude values ​​and the normal incident reflection time. This method can accurately measure the sound velocity and thickness of the skull in situ.

Provided are a signal transmission method, a communication apparatus, a storage medium and a program product. The method comprises: determining array elements in a coprime array, wherein the array elements in the coprime array are some array elements in an antenna array of a first communication apparatus; and on the basis of hybrid beamforming, transmitting a first signal by means of the coprime array. The coprime array can achieve a larger virtual aperture by means of fewer physical array elements, or in other words, the number of equivalent virtual array elements is usually greater than the number of physical array elements. In this way, a transmitter of the first signal can use fewer array elements to implement beamforming of more signals; and a receiver of the first signal can use fewer array elements to implement DOA estimation of more signals, thereby improving the degrees of freedom of DOA estimation.

The application discloses a conventional beam forming learning method based on a deep convolutional neural regression network, comprising the following steps: step 1, obtaining a data set, comprising a plurality of data pairs, any data pair comprising array element domain frequency domain data and corresponding spatial energy spectrum; step 2, performing feature pre-extraction on the array element domain frequency domain data to obtain array element domain frequency domain features and construct a sample set; step 3, constructing a deep convolutional neural network model with the array element domain frequency domain features as input and the corresponding spatial energy spectrum as target output; step 4, training the deep convolutional neural network model based on the sample set; and step 5, after performing feature pre-extraction on unknown array element domain frequency domain data, inputting the unknown array element domain frequency domain data into the trained deep convolutional neural network model to output a predicted spatial energy spectrum. The method and model disclosed by the application are a conventional beam forming framework independent of current array signal processing methods, and have the characteristics of autonomous learning evolution, efficient parallel computing and the like.

The invention provides an ultrasonic imaging method based on a single-angle plane wave and a convolutional neural network, and belongs to the technical field of neural networks and ultrasonic imaging, and the method comprises the steps: obtaining the original radio frequency data of the single-angle plane wave, and enabling all array elements of an ultrasonic probe to synchronously transmit and receive echo signals to generate the original radio frequency data; processing the original radio frequency data through a convolutional neural network model, fitting a focused wave imaging effect through supervised learning by the convolutional neural network model, and generating a target ultrasonic imaging image; and outputting a target ultrasonic imaging image, wherein the frame rate of the target ultrasonic imaging image is higher than that of traditional focused wave imaging. According to the invention, for the business scene problems of low frame rate and insufficient real-time performance of traditional focused wave imaging, high-frame-rate and high-quality ultrasonic imaging is realized by innovatively combining rapid acquisition and deep learning optimization of single-angle plane waves.

The application discloses a kind of implementation methods of large-scale phased array simulation instrument, including by designing signal processing module and gigabit network interface cooperation use, make single camera control simulation instrument gauge realize the simulation of thousands of array scale under the premise of 64 physical ports.The application breaks through the bottleneck of traditional phased array simulation instrument relying on hardware stacking by innovative signal processing architecture and high-speed data interface technology.Compared with the scheme that traditional method needs to simulate large-scale array by increasing physical port or complex cascade of multiple devices, the application realizes the leap of efficiently simulating thousands of array elements scale with single integrated device, which not only greatly reduces the hardware cost of system construction, but also fundamentally simplifies the system topology, avoids complex wiring and synchronization problems, thereby providing an efficient and flexible solution for large-scale array simulation required by satellite communication, sensing integration and other frontier technical fields with higher cost performance and reliability.

The invention provides a target recognition method and system based on deep learning. The method comprises the steps that S1, an active sonar array element domain signal is converted into a two-dimensional echo image after beamforming and matched filtering; s2, splicing a plurality of continuous frames of two-dimensional echo images to obtain a beam accumulation image; intercepting a target area in the beam accumulation image to obtain a beam dimension accumulation time stream image; and S3, inputting the beam dimension accumulation time flow image into a deep learning model, and outputting a target identification result. The motion information of a target is explicitly embedded into an input image through multi-period joint accumulation of a wave beam dimension, so that a deep learning model can efficiently extract physical related characteristics, and relatively high recognition accuracy and robustness can still be kept under a complex marine environment and a small sample condition.

The invention belongs to the technical field of chip algorithms, and particularly relates to a circular array beam forming method and system based on a domestic FPGA. Comprising the following steps: receiving multiple paths of array element signals, and performing frequency mixing and filtering to obtain baseband I / Q data; the base band I / Q data is unpacked to be distributed and stored in a plurality of distributed memories in an FPGA (Field Programmable Gate Array) processing unit; the method comprises the following steps: reading baseband I / Q data of a plurality of channels and corresponding phase shift coefficients in parallel, and carrying out multiplication, accumulation, summation and modulo operation to generate data of a beam point; repeating the calculation step of generating the beam point data by updating the phase shift coefficient so as to generate beam domain data; reordering the calculated sequence of the beam domain data, and adjusting the output sequence of the beam domain data; the method has the advantages of being high in processing speed, high in anti-noise capacity and the like on the basis of meeting domestic substitution.

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

The present application relates to the technical field of DOA estimation, and particularly relates to a wireless communication data processing method based on artificial intelligence. The method comprises the following steps: obtaining first probabilities corresponding to each pixel point in a connected domain of a target object in an acquired factory ground image according to the coordinates of the pixel points; constructing a semantic distribution probability graph according to the first probabilities corresponding to each pixel point in the connected domain in the factory ground image; constructing an RSSI sequence according to RSSI values corresponding to each RSSI receiver; inputting correlation coefficients of left array element RSSI differential sequences and right array element RSSI differential sequences corresponding to the RSSI sequence into a DOA range estimation network to construct an angle range probability graph corresponding to the target object; and obtaining a DOA corresponding to the target object according to a semantic distribution probability graph, an angle range probability graph and an RSSI sequence corresponding to the target object. The present application uses computer vision to assist wireless communication to realize DOA estimation, so that the obtained DOA is more accurate.

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.