53 results about "Hadamard matrix" patented technology

The invention relates to the technical field of image processing, in particular to a compression imaging method and system based on a Swin Transform network, and the method comprises the steps: carrying out the sampling processing of training data through employing a CC Hadamard matrix, obtaining a coding image, training a neural network based on the Swin Transform through employing the coding image, and obtaining an optimal compression reconstruction model; in the system, the optimal compression reconstruction model is loaded into a processing center, multiple low-resolution images collected by a detector are preprocessed to obtain coded images, and then the optimal compression reconstruction model is utilized to obtain reconstructed images. Compared with a common sampling matrix, the system and the method provided by the invention have the advantages that the reconstruction effect after sampling of the CC Hadamard matrix is better, and the reconstruction quality of the image is effectively improved.

The invention discloses an underwater correlated imaging system and method based on secondary modulation, and relates to the technical field of underwater photoelectric imaging. In the system, an optical transmitting and guiding unit transmits a laser signal, a signal receiving and processing unit generates a Hadamard matrix speckle sequence, and a control unit drives a swing mirror DMD modulation device to swing according to a preset angle; the oscillating mirror DMD modulation device scans a detection target according to a preset angle and determines sub-view fields at different angles; performing first modulation on the first reflection signal based on the reference Hadamard matrix speckle to generate a first modulation optical signal; irradiating the detection target by using the first modulation optical signal, and performing second modulation on the optical signal reflected by the detection target based on the reference Hadamard inverse matrix speckle to generate a second modulation optical signal; and the signal receiving and processing unit obtains a target image based on the second modulation light signal and the reference Hadamard matrix speckle in each sub-view field. According to the invention, the signal-to-noise ratio of underwater correlated imaging is improved.

The present invention belongs to the field of signal processing technology and relates to a non-reconstruction direct positioning method based on mobile receiver data compression. The present invention aims to provide a non-reconstruction direct positioning method based on mobile receiver data compression. This method utilizes the characteristics of the Hadamard matrix to directly estimate the source position in the compressed measurement domain without the need for signal reconstruction and TDOA and FDOA parameter extraction. The present invention demonstrates that signals can still retain TDOA and FDOA information after Hadamard matrix compression, thereby reducing transmission throughput while maintaining positioning accuracy.

The invention discloses a dynamic cross-modal Hash retrieval method and system based on concept vector learning. The method comprises the following steps: constructing a concept vector matrix based on current label information and a Hadamard matrix; concept vectors in the concept vector matrix are used for representing that the samples with the same label have the same and unchanged vectors; constructing a data similarity matrix based on the current label information and the historical label information; obtaining a target hash code based on the concept vector matrix and the data similarity matrix; obtaining a target hash function based on the target hash code; analyzing the to-be-queried data according to the target hash function to obtain a to-be-queried hash code corresponding to the to-be-queried data; and performing similarity calculation on the to-be-queried hash code and a target hash code in a database to obtain a target retrieval result. According to the method, the defect of the concept drift problem in the dynamic data environment can be overcome, the adaptability of the cross-modal Hash model to the concept drift is improved, the dynamic cross-modal Hash retrieval performance is improved, and the method can be widely applied to the technical field of information processing.

The invention discloses a correction method for a Hadamard transform spectrometer, and relates to the technical field of optical imaging, and the method comprises the steps: S1, determining a Hadamard matrix, and constructing an ideal coding matrix; s2, obtaining an actual coding matrix from the ideal coding matrix according to the crosstalk influence model, namely S21, dividing the ideal coding matrix into a left ideal coding matrix and a right ideal coding matrix, determining a left actual coding coefficient and a right actual coding coefficient, and obtaining the left actual coding matrix, the right actual coding matrix and the actual coding matrix containing the coefficients; s22, establishing association among the left and right actual coding coefficients, the abnormal peak intensity value and the input spectrum; s23, measuring a group of input spectrums to obtain a measured value and an abnormal peak intensity value, calculating a left actual coding coefficient and a right actual coding coefficient, and obtaining an actual coding matrix; s3, when any spectrum is subjected to Hadamard coding measurement, the actual coding matrix is used for recovery, and a corrected spectrum is obtained. According to the method, the actual coding matrix is constructed and used for correcting spectral measurement, and the problem of measurement deviation caused by interference between wavelengths is solved.

The invention belongs to the technical field of computational imaging, and particularly relates to a physical information hybrid constraint-based parallel convolution single-pixel detector imaging method, which comprises the following steps of: S1, prefabricating a coding matrix by using a Hadamard matrix as a two-dimensional structured modulation light field; s2, obtaining one-dimensional light intensity coding information of a target scene through the output voltage of the single-pixel detector, carrying out parallel matrix, and establishing a parallel convolutional neural network model; s3, constructing a physical information constraint equation used for optimizing convolution parameters of the parallel convolutional neural network model; and S4, determining a physical information hybrid constraint condition, optimizing a convolution kernel of the parallel convolutional neural network model, and obtaining a target scene image. According to the method, neural network model parameters can be minimized in the imaging process of the single-pixel detector, the number of iterations is reduced, the imaging time is shortened, real-time single-pixel imaging under physical information driving is achieved, and the single-pixel detector can have higher imaging quality at a low sampling rate by introducing physical mixed constraints.

The invention discloses a self-adaptive anti-interference scheduling method and system for a wireless ad hoc network, and relates to the technical field of wireless communication, and the method comprises the steps: collecting a service flow in the wireless ad hoc network in real time, and monitoring data environment parameters which comprise interference intensity and a network load rate; calculating a basic resource rate by using a Mie equation according to the interference intensity, dynamically adjusting parameters of the Mie equation according to a network load rate, and generating a dynamic total resource amount; constructing an orthogonal ARU matrix according to the total amount of the dynamic resources, constraining ARU correlation coefficients of adjacent nodes through a Hadamard matrix, and generating a conflict-free ARU allocation matrix; and splitting the service flow into atomic data blocks, mapping the atomic data blocks to the conflict-free ARU allocation matrix by adopting a Hungary algorithm, and generating a three-dimensional resource scheduling table. According to the method, the resource allocation precision and the system robustness are improved; the multi-dimensional resource conflict probability is reduced, and the spectrum multiplexing efficiency and the network stability are improved.

The present invention discloses an undersampled wideband array signal processing method based on an improved Hadamard matrix. The method includes establishing an array receiving narrowband signal model and a spatial domain compressive sensing observation model under the array, and proposing a compressive zeroing observation matrix based on sequential partial Hadamard; using the spatial domain sparse adaptive OMP algorithm and the spatial domain sparsity of the array received signal to jointly reconstruct and estimate the arrival direction of the underwater target signal; and extending this method to process underwater wideband signals, and proposing an undersampled wideband array signal processing method based on an improved Hadamard matrix with engineering practical significance. This method can be used as a means for active sonar to reduce the number of array elements in actual engineering when using LFM signals to detect targets, and can be used in fields such as underwater target azimuth capture and tracking.

The invention discloses a method for decoding and quantifying a sequential artery spin labeling image, and belongs to the technical field of magnetic resonance imaging. The method comprises the following steps: firstly, acquiring to-be-decoded Hadamard time sequence pCASL data consisting of coded signal images; then decoding the acquired N coded signal images by using the decoding matrix to obtain a multi-time-point pCASL signal image formed by the decoded signal images; wherein the decoding matrix is obtained by performing matrix multiplication operation on an upper triangular matrix and a Hadamard matrix and then removing a first row. According to the invention, the signal strength and the signal-to-noise ratio of the decoded image can be improved, the artery transmission time estimation under different scanning protocols is realized, and the application range is wide.

The present application provides an efficient Hadamard decoding method, device, storage medium, and electronic device suitable for high-speed parallel processing, relating to the field of decoding technology. The method comprises: calculating the inner product of all Hadamard codewords and channel input LLR values ​​using an FHT algorithm; grouping the bits to be decoded in pairs according to the symmetry characteristics of the Hadamard matrix to obtain multiple bit groups, wherein the bit groups include a first bit and a second bit; performing internal interleaving processing on each bit group, and calculating the maximum a posteriori probability value of the first bit and the second bit in each bit group based on the interleaving situation and the inner product to complete the decoding. The technical solution of the present application obtains the same decoding result while reducing comparisons, thereby reducing hardware resource consumption.

The invention provides a two-dimensional coding and decoding method for ultrasonic synthetic aperture imaging. The method comprises the following steps of: generating an order Hadamard matrix, which is a multiple of 8, for an ultrasonic transducer array with an array element; according to the requirements of the axial resolution and the near-field dead zone range, a level serial coding matrix is generated; a Hadamard matrix and a serial coding matrix are used to generate a two-dimensional coding matrix through matrix multiplication, and common coding emission and acquisition are carried out; performing dual decoding on the acquired echo data according to the two-dimensional coding matrix; and performing synthetic aperture imaging by using the decoded data. According to the invention, the depth and contrast of ultrasonic synthetic aperture imaging can be improved.

The application discloses a dynamic cross-modal hash retrieval method and system based on concept vector learning, and the method comprises the following steps: constructing a concept vector matrix based on current label information and a Hadamard matrix; the concept vectors in the concept vector matrix are used for representing that samples with the same label have the same and invariable vectors; constructing a data similarity matrix based on current label information and historical label information; obtaining target hash codes based on the concept vector matrix and the data similarity matrix; obtaining a target hash function based on the target hash codes; analyzing the to-be-queried data according to the target hash function to obtain to-be-queried hash codes corresponding to the to-be-queried data; and performing similarity calculation on the to-be-queried hash codes and the target hash codes in a database to obtain target retrieval results. The application can overcome the defects of the concept drift problem in a dynamic data environment, improve the adaptability of a cross-modal hash model to the concept drift, improve the dynamic cross-modal hash retrieval performance, and can be widely applied to the technical field of information processing.

The invention discloses a signal depth compression and direct positioning method, system and device and a medium, and the method comprises the steps: arranging at least three observation stations in a two-dimensional space, carrying out the synchronous observation of a signal transmitted by a target, carrying out the sampling compression through a Hadamard matrix, and carrying out the quantification compression through a 1-bit quantification means, and achieving the depth compression of an observation signal; according to the method, the characteristics of a Hadamard matrix and the phase invariance of 1-bit compression are utilized, a complex signal reconstruction process is avoided, position parameters such as time delay do not need to be extracted, and a cost function is directly established from deeply compressed data by utilizing a reserved time delay relation so as to determine the position of a target; the system, the device and the medium can realize signal depth compression and direct positioning based on the method. The method can remarkably shorten the data volume of signal transmission, simplifies the processing process, and is suitable for positioning scenes with limited communication bandwidth and need of quick response; on the premise of guaranteeing the positioning precision, the signal data volume is greatly compressed, and the communication bandwidth requirement for transmitting the received signal of each station to the central station is reduced.

The application belongs to the technical field of quantum imaging of computational imaging, and specifically discloses a correlation imaging reconstruction algorithm based on a deep convolutional hybrid neural network, which uses a Hadamard matrix gray scale coding sequence to replace a Gaussian matrix speckle sequence of original correlation imaging, and adjusts a digital micromirror device with a fixed sequence of speckle sequences in data acquisition and processing of correlation imaging. Under the same sampling rate, the algorithm can reconstruct a high-quality target object image, and has higher PSNR and SSIM values. Deep learning realizes end-to-end image recovery of an image in the algorithm implementation of correlation imaging, and reduces the steps of image calculation.

The application discloses a kind of ghost imaging reconstruction and classification method and equipment based on hybrid network, belong to the field of computational imaging technology, will be based on Hadamard matrix generation light speckle sequence formed by fixed light speckle sequence;Then, based on this light speckle sequence sampling expanded MNIST data set, generate light intensity sequence, light speckle sequence data;Then, construct and train GRU-CNN network model;Finally, using the model trained to calculate correlation imaging reconstruction and target identification classification.The conventional ghost imaging technique is often faced with the problem of poor image quality under low sampling conditions.In order to solve this problem, the application proposes a GRU-CNN network (GCGI) model combined with deep learning, which can efficiently reconstruct high-quality images and accurately identify and classify targets under very low sampling rates by fusing light intensity sequence and light speckle information, and has strong practical value.

The invention discloses a TCM code parameter blind identification method and device and a storage medium, and belongs to the field of electronic signal reconnaissance and confrontation. The method comprises the following steps: S1, acquiring binary code stream data, an initial code length and an initial constraint length of TCM coding; s2, constructing a binary linear equation set, and obtaining a mapping vector; s3, segmenting the mapping vector, and then constructing a Hadamard matrix; s4, carrying out Walsh-Hadamard transformation on the segmented vectors, so as to obtain a value of a Walsh spectrum; s5, searching a Walsh spectrum peak value outside the 0 point position, if the Walsh spectrum peak value is searched, obtaining a correct code length and a correct constraint length according to the position of the Walsh spectrum peak, and executing S6; if the Walsh spectrum peak value is not found, adding 1 to the initial code length, returning to S2, and continuing to execute the subsequent steps; and S6, constructing a new binary linear equation set, performing Walsh-Hadamard transformation according to the correct code length and the correct constraint length to obtain a correct initial position and a correct check matrix, and outputting an identification result. The method is low in calculation complexity, easy in engineering implementation and high in operation speed.

The invention provides an interference communication integrated signal design method based on MIMO-OCDM (Multiple Input Multiple Output-Optical Code Division Multiplexing). The implementation steps are as follows: constructing an application scene of an interference communication integrated signal and a binary communication data matrix; performing OCDM on the binary communication data matrix, and obtaining an MIMO-OCDM communication signal and a frequency shift interference signal; and finally, an MIMO-OCDM interference communication integrated signal is obtained. According to the method, Hadamard matrix coding is carried out on the time domain signal matrix obtained after discrete Fresnel inverse transformation is carried out on the frequency domain QPSK symbol matrix so as to obtain the MIMO-OCDM communication signal matrix, it is guaranteed that multiple time domain signals have the high anti-interference capacity, interference of signals between MIMO transmitting channels is reduced, and the communication performance of the MIMO-OCDM communication signal matrix is improved. Moreover, a non-partner needs to obtain the interference communication integrated signals transmitted by all MIMO transmission channels and correctly analyze and recombine the interference communication integrated signals to obtain the communication data, thereby improving the anti-interference difficulty of the radar of the opposite side.

The invention proposes an OTFS anti-interference transmission method and system and a computer readable storage medium, and the system carries out the sparse arrangement of user service data in a time delay Doppler domain, carries out the homogenization processing through the multiplication with a Hadamard matrix, carries out the processing according to a conventional OTFS signal transmission process, and carries out the transmission of an OTFS signal. A receiving end firstly converts received OTFS signals into a time-frequency domain, then selects signals lower than an interference threshold value to form a dimensionality-reduced observation signal matrix, and then reconstructs a sparse signal matrix in an original time delay Doppler domain by using a compressed sensing algorithm, thereby obtaining user service signals. According to the method, interference is filtered in the time-frequency domain, interference is smoothed in the time delay Doppler domain, the capability of an OTFS system for resisting external narrow-band intentional interference is greatly improved, and dual-robustness transmission for time-varying fading and external interference is achieved.

The invention relates to the field of array signal processing, in particular to a method for estimating DOA in a time modulation array based on a GPH matrix scheme. In order to reduce energy dispersion of antenna radiation and expand a direction finding range, the invention provides a DOA estimation method of a time adjustment array based on a GPH scheme. Based on the GPH matrix, the complete complementary set is obtained through recursive derivation of the Hardman matrix, the mutually paired sequences with the same number of conducted radio frequency switches are selected from the complete complementary set, the time modulation array is obtained through design, the direction finding range is expanded, the DOA estimation performance is improved, and high-precision and low-complexity switching antenna array DOA estimation is achieved.

The present invention provides a method for processing EEG signals with a high compression rate, comprising the following steps: first, obtaining the original EEG signal, then converting it into a sparse domain through a sparse transform such as a discrete cosine transform and extracting sparse coefficients. Next, constructing an initial correlation matrix, and generating an extended matrix through vertical and horizontal expansion and nesting operations to meet the requirements of compressed sensing. Finally, the sparse coefficients are compressed using the extended matrix to obtain a compressed signal representation. The method of the present invention improves the compression rate and reduces the hardware power consumption by designing an extended measurement matrix based on a combination of Hadamard matrices, solving the problem of resource consumption in the online generation of random sparse matrices in the prior art, and is particularly suitable for low-power demand scenarios, thereby improving the hardware performance of the EEG signal processing solution.

The invention provides an efficient Hadamard decoding method and device suitable for high-speed parallel processing, a storage medium and electronic equipment, and relates to the technical field of decoding, and the method comprises the steps: calculating an inner product of all Hadamard code words and a channel input LLR value through an FHT algorithm; according to the symmetry characteristic of a Hadamard matrix, bits needing to be decoded are grouped in pairs, a plurality of bit groups are obtained, and each bit group comprises a first bit and a second bit; and performing internal interleaving processing on each bit group, and calculating the maximum posterior probability value of the first bit and the second bit in each bit group according to the interleaving condition and the inner product to complete decoding. According to the technical scheme, the same decoding result is obtained under the condition that comparison is reduced, and hardware resource consumption is reduced.

The invention relates to a deep semantic embedding method and device based on a cross-environment separated architecture, and the method comprises the steps: a cloud side generates global semantic embedding through a pre-training language model and a Hadamard matrix based on a class label text, mines a label hierarchical structure through a double-block attention mechanism, and obtains a classification label; generating a high-level semantic hash code containing intra-layer similarity and cross-layer correlation, and constructing an anchor point library; the edge device receives an anchor point library and global semantic embedding, learns a low-level semantic hash code through a discrete cyclic coordinate descent algorithm, and introduces an instance-level fine-grained weight to dynamically distribute a multi-modal data fusion weight; a cloud and an edge device record a corresponding relation of high-layer and low-layer hash codes through a dynamic semantic mapping table, a bit-by-bit calibration algorithm corrects cross-environment deviation, and the edge device only transmits hash code bits different from an anchor point library and compresses semantic information in combination with category-level high-level codes, so that deep semantic embedding with consistent cross-environment semantics is realized. Cloud precision and edge real-time performance are balanced, and efficient retrieval of multi-modal data is supported.

The present application belongs to the technical field of calibration of phased array radar, and relates to a two-stage grouping asynchronous rotating electric vector radar array antenna calibration method, and the specific process is as follows: an attenuator and a phase shifter are configured to each element channel of the array antenna to be measured, and signals received by all channels are synthesized through a power divider; the phased array radar to be measured is equally grouped by channel number in a first stage; a grouping matrix is constructed based on a Hadamard matrix, and the channels grouped in the first stage are divided into rotating groups and fixed groups according to elements in the grouping matrix; a phase shift matrix for phase shift measurement of each row in different grouping matrices is constructed based on a discrete Fourier transform matrix with an order of N, and phase shift is performed; linear equations are constructed according to measurement results and solved, and the ambiguity coefficient, the amplitude of each channel and the phase difference between each channel are obtained, and each field normalization amplitude and phase is solved; amplitude and phase compensation of the channels is completed based on each field normalization amplitude and phase, and finally the amplitude and phase of all channels are consistent.

The embodiment of the invention provides a semantic communication method and device in a multi-user scene, equipment and a storage medium, and belongs to the technical field of communication. The method comprises the following steps: adjusting an inner product among a plurality of initial orthogonal code words in an obtained Hadamard matrix to determine a first identity code of each transmitter, so that the first identity codes of different transmitters are in low correlation; any transmitter performs coding processing on the initial information according to the first identity code and a first signal to noise ratio in the current transmission environment to obtain semantic coding information, and performs adaptive modulation processing on the semantic coding information to obtain coding feature information; and the transmitter sends the coding feature information to a receiver in communication connection with the transmitter, so that the receiver performs adaptive demodulation processing and decoding processing on the coding feature information to obtain target information matched with the initial information after receiving the coding feature information sent by the transmitter. According to the method, the semantic communication quality in a multi-user scene can be improved under the condition that resources are limited.

The invention discloses a sonde energy-saving method based on a superposition coding technology, and belongs to the field of wireless communication. In order to solve the problem that energy consumption is too high due to the fact that a wireless communication module is frequently started by a traditional sonde, superposition coding transmission of meteorological data is achieved through a Hadamard orthogonal matrix. The method specifically comprises the steps that after meteorological data packets collected at multiple adjacent moments are subjected to constellation modulation, Hadamard matrix row vectors are distributed for signal expansion; superposing the extended signals into a single transmitting signal, and completing multi-packet data transmission through single communication; and the ground station demodulates and recovers each data packet by using the orthogonality of the row vector of the Hadamard matrix. A dynamic adjustment strategy is innovatively introduced, the number of superposed packets is optimized in real time according to the bit error rate feedback of a ground station, and energy is saved to the maximum on the premise that the communication quality is guaranteed. According to the method, the communication energy consumption is reduced, the endurance of the lithium battery is effectively prolonged, the method is suitable for an aerial sonde system released by an unmanned aerial vehicle, and the meteorological data acquisition efficiency is remarkably improved.

A computer-implemented method for simulation of a given quantum circuit, the method comprising separating the quantum circuit into layers of quantum gates of a common gate class; decomposing each of the layers of quantum gates of the common gate class into a sequence of base operations, the base operations being selected from a diagonal matrix operation based on unitary diagonal operations applied in parallel to qubits of the quantum circuit, a permutation matrix operation expressible as a matrix with exactly one entry of 1 in every row and column, with the rest being zero, and a Hadamard matrix operation based on Hadamard gates applied in parallel to qubits of the quantum circuit that transforms a plurality of the qubits between two orthogonal bases; and evolving a quantum state vector according to the layers decomposed into the sequence of base operations.

The invention discloses a calculation ghost imaging reconstruction method based on a Vision Transform and a U-Net network, and the method comprises the following steps: 1, enabling a Hadamard matrix of a cut cake sequence to serve as a phase mask, sequentially embedding the Hadamard matrix into a spatial light modulator, sequentially projecting the Hadamard matrix to a target image, and collecting a total light intensity sequence through a barrel detector; 2, partitioning the Hadamard matrix of the cake cutting sequence and the light intensity sequence, performing linear projection on each block to obtain a high-dimensional vector token, adding a position code to each high-dimensional vector token, inputting the high-dimensional vector token into a Transform encoder for processing, and converting the output into a two-dimensional matrix; and step 3, inputting the two-dimensional matrix obtained in the step 2 into the U-Net network to enhance transmission and fusion of feature information until a loss function of the ViT-U-Net network is kept stable, and obtaining a result of calculating ghost imaging. The image reconstructed by the method is high in quality and high in reconstruction efficiency.

The invention relates to a camera pixel super-resolution method, which comprises a camera and a plurality of different masks, a plurality of low-resolution pictures are shot through the cooperation of the camera and the masks, and a high-resolution picture is reconstructed through a super-resolution recovery algorithm. The different masks are formed by rearranging different row vectors in a Hadamard matrix, and light field information of a sub-pixel level is coded; the mask coding uses compressed sensing to greatly reduce coding times; the camera photographs different low-resolution pictures encoded by different masks, and the super-resolution recovery algorithm constructs a pseudo-inverse matrix of a measurement matrix through singular value decomposition (SVD) to further improve an iterative hard threshold (IHT) algorithm, achieving more efficient reconstruction. According to the method, the super-resolution of the intensity image shot by the camera can be realized, and the super-resolution of the phase image shot by the camera can also be realized, that is, the method can be combined with a phase imaging technology, so that the application of the industrial camera in the biomedical field is expanded.

A signal depth compression and direct positioning method, system, device and medium, the method is: by arranging at least three observation stations in two-dimensional space, synchronously observing the signal emitted by the target, using Hadamard matrix for sampling compression and 1-bit quantization means for quantization compression, realizing the depth compression processing of the observed signal; using the characteristics of Hadamard matrix and the phase invariance of 1-bit compression, avoiding the complex signal reconstruction process, without extracting position parameters such as time delay, directly establishing a cost function from the depth compressed data using the retained time delay relationship to determine the position of the target; the system, device and medium can realize signal depth compression and direct positioning based on the above method; the present application can significantly shorten the data amount of signal transmission and simplify the processing process, and is suitable for positioning scenes with limited communication bandwidth and the need for fast response; on the premise of ensuring positioning accuracy, the signal data amount is greatly compressed, and the communication bandwidth requirement of each station receiving signal transmission to the center station is reduced.

The invention discloses a spatial channel feature identification method based on compression measurement, and belongs to the technical field of electromagnetic information and multi-antenna wireless communication. According to the method, the random singular value decomposition and compressed sensing theories are combined, programmable array antennas are configured at a transmitting end and a receiving end respectively, the orthogonality of a Hadamard matrix is utilized to realize double-side compressed projection measurement, and a low-dimensional observation matrix is obtained only through a small amount of coding measurement. The main mode of the original space can be deduced by performing singular value decomposition on the matrix, and high-precision estimation of key features such as channel degree of freedom is realized. Compared with a traditional full-amount measurement method, the method has the advantages that the measurement complexity is reduced, the test time and resource consumption are remarkably reduced, and the method has the advantages of being friendly in hardware, high in robustness, high in engineering practicability and the like, and is suitable for scenes such as a large-scale MIMO system, millimeter wave channel modeling and intelligent wireless network optimization.