347 results about "Compressed sensing" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention discloses a multi-medium three-dimensional flaw detection system based on ultrasonic pulse echoes. The multi-medium three-dimensional flaw detection system comprises a synchronous data acquisition module, a temperature compensation module, a multi-physical field simulation optimization module, a signal processing module and a three-dimensional imaging and output module, the synchronous data acquisition module scans an object, records coordinates and synchronously transmits and receives ultrasonic echoes; the temperature compensation module calculates real-time sound velocity according to the water temperature, the real-time sound velocity is used by the multi-physics field simulation module, and the multi-physics field simulation module simulates sound wave propagation and generates a defect-free theoretical waveform as a reference by combining intrinsic parameters of an object and sound wave propagation parameters; the signal processing module carries out processing and feature extraction on actually measured echoes, and compares the actually measured echoes with a reference to identify defect signals; and the three-dimensional imaging module associates the signals with the coordinates, a three-dimensional model is generated by adopting slice reconstruction and a compressed sensing algorithm, and defects are visualized, so that the precision, the efficiency and the environmental adaptability of three-dimensional flaw detection can be improved.

A power transmission tower grounding resistance intelligent measurement method based on ground potential field distributed sensing comprises the steps that a ground potential sensing network is arranged around a power transmission tower grounding device, and a ground potential field distributed measurement system with an iron tower as the center is established; a broadband characteristic coding current signal is injected into the grounding system through a weak current injection module; acquiring potential response data of each node of the ground potential sensing network excited by the feature coding current signal by adopting a distributed synchronous acquisition technology, and reconstructing complete ground potential field spatial distribution from sparse node observation data based on a compressed sensing theory through a signal reconstruction algorithm; and inputting the reconstructed complete ground potential field spatial distribution map into a pre-trained map neural network model, and outputting an intelligent identification result and an anomaly diagnosis conclusion of the ground resistance. According to the invention, non-contact, distributed and high-precision grounding resistance measurement under the condition that a grounding lead does not need to be disconnected is realized, and the measurement precision and reliability under a complex working condition are remarkably improved.

The invention provides a geomembrane on-line leakage detection system and detection method, and relates to the technical field of geomembrane detection, the system comprises a signal monitoring module used for arranging a sensing detection network under a geomembrane in an array, carrying out on-line monitoring, and determining a real-time leakage signal of the geomembrane; the sensing and positioning module is used for executing compressed sensing and positioning based on sparse reconstruction according to a plurality of leakage points corresponding to the plurality of leakage signals, and identifying a source sensing matrix; and the leakage early warning module is used for carrying out leakage point early warning based on the source sensing matrix and carrying out leaked area early warning based on the plurality of leakage signals. The technical problems that in the prior art, geomembrane leakage is difficult to accurately recognize and position, response is not timely, and the anti-seepage safety and the management efficiency are reduced are solved. The technical effects of accurately positioning and early warning the leakage points in real time, effectively improving the sensitivity and response speed of geomembrane leakage detection, and improving the anti-seepage safety and management efficiency are achieved.

The invention relates to the field of digital information transmission, in particular to an OTFS channel estimation method based on dynamic sparse Bayesian under a low earth orbit satellite, which comprises the following steps of: converting an information symbol and a pilot symbol into a time delay Doppler domain sending matrix; sending the time delay Doppler domain sending matrix to a receiving end; dividing a pilot signal of a receiving end into a channel vector and a verification pilot signal; modeling a channel vector into a compressed sensing problem based on an OTFS system, and solving the compressed sensing problem by adopting dynamic sparse Bayesian channel estimation to obtain a posterior channel vector; iteratively updating the hyper-parameter of the posterior channel vector to obtain an optimal channel estimation result; performing iterative optimization on a channel estimation result by adopting a verification pilot signal to obtain an estimated channel vector; the OTFS channel estimation method designed by the invention has relatively strong anti-noise and anti-interference capabilities, and the estimation precision is remarkably superior to that of a traditional sparse Bayesian learning method.

The invention relates to the technical field of wireless communication and signal processing, in particular to a short-wave spectrum efficient sampling method and system based on compressed sensing, and the method comprises the steps: collecting full-band power spectrum density data through a low-speed scanning receiver; determining a strong interference frequency point set; generating a binary null observation sequence; loading the binary null observation sequence to an analog front-end frequency mixing circuit, performing analog domain orthogonal frequency mixing and integral processing on the input radio frequency signal, and outputting a voltage sampling value; resolving to obtain a signal sparse coefficient through a compressed sensing reconstruction algorithm; calculating a reconstruction residual error according to the voltage sampling value and the signal sparse coefficient; in response to the condition that the reconstruction residual error is greater than a preset error threshold, triggering a reacquisition process of the power spectral density data; outputting a signal sparse coefficient in response to the condition that the reconstruction residual error is smaller than or equal to a preset error threshold; according to the invention, the problems of limited dynamic range and weak signal submerging are solved, and the receiving sensitivity of weak signals in a strong interference environment is obviously improved.

The invention relates to the technical field of image encryption, in particular to a block compressed sensing-combined chaotic image encryption method, which comprises the following steps of: S10, acquiring a plaintext grayscale image, calculating a hash value of the plaintext grayscale image by using an SHA-512 algorithm and generating an initial secret key; s20, constructing a measurement matrix required by block compressed sensing and a chaos sequence and a matrix required by an image encryption algorithm by using the chaos sequence iterated by the two-dimensional composite chaos mapping and the initial secret key; s30, performing block processing on the plaintext grayscale image P, measuring each image block by using the measurement matrix, and combining the processed blocks into a new matrix; s40, the new matrix is subjected to scrambling operation based on DNA and S-box, and a ciphertext is obtained; s50, performing diffusion operation on the ciphertext to obtain a final encrypted image CI; s60, decrypting the encrypted image according to an inverse process of image encryption, and reconstructing the decrypted image by using an SL0 algorithm to obtain a final reconstructed image; the algorithm has good safety performance.

The invention relates to the technical field of image reconstruction, in particular to an intelligent trolley image compressed sensing reconstruction method and system based on deformable convolution. The method comprises the following steps: an intelligent trolley obtains multiple frames of original images in a driving scene through a vehicle-mounted image acquisition module and carries out preprocessing to generate a preprocessed image set; performing feature extraction, feature matching, hypergraph transformation and image synthesis operation on the basis of the preprocessed image set, performing compressed sensing sampling processing at the same time to obtain compressed sensing sampling data, and inputting the compressed sensing sampling data into a preset compressed sensing reconstruction network; a deformable convolution module is embedded in the front end of the compressed sensing reconstruction network for feature extraction and image detail recovery calculation, and a preliminary reconstruction image is generated; and embedding a deformable deconvolution module at the rear end of the compressed sensing reconstruction network to carry out pixel-level adjustment and quality evaluation until a final reconstruction image meeting the reconstruction effect requirement is generated. According to the invention, the precision of vehicle-mounted image compressed sensing reconstruction can be improved.

A codeword-native database management platform enables efficient processing of database operations by working directly with compressed data. The system implements multiple compression schemes including Huffman, alphabetic, and neural compression, with support for conditional variants and constraints. A hybrid architecture manages both compressed and uncompressed data formats, allowing for seamless operation across different data representations. The system includes specialized components for query processing, storage management, and client-server communication, all optimized for compressed data operations. Query execution plans are generated to minimize decompression requirements while maintaining performance efficiency. The system employs compression-aware buffer management and indexing strategies, enabling direct operations on compressed keys. This approach significantly reduces storage requirements and improves query performance by eliminating unnecessary decompression cycles, while maintaining full ACID compliance and supporting existing database functionalities.

The invention discloses a crack strong-robustness time-frequency compressed sensing monitoring method, system, medium and equipment based on inter-blade tip timing, and the method comprises the steps: determining the installation number and installation positions of blade tip timing sensors, and carrying out the circumferential installation of the blade tip timing sensors on a rotor blade case; the blade tip timing sensors are used for obtaining a time sequence when the rotor blades reach the positions of the blade tip timing sensors; based on each time sequence and the rotor blade included angle, obtaining an inter-blade tip coupling vibration speed measured by each blade tip timing sensor; an undersampling vibration speed sparse representation model is constructed based on the inter-blade tip coupling vibration speed, dynamic frequencies of adjacent rotor blades are decoupled and extracted by using a sparse reconstruction algorithm, and non-baseline monitoring is performed on a crack propagation process according to the evolution of a vibration frequency difference between the blades.

The invention relates to the technical field of medical instrument volume detection, and discloses a medical instrument internal volume detection system. A continuous wave and a pulse modulation signal are simultaneously transmitted through an annular phased array microwave antenna array, three-dimensional dielectric constant distribution is reconstructed by adopting a compressed sensing algorithm, and Doppler time-frequency domain characteristics are extracted through short-time Fourier transform; and inputting the phase gradient field and Doppler velocity field features into a convolutional neural network to generate a four-dimensional space-time volume evolution model, separating a macroscopic volume trend and a microcosmic pulsation component by adopting an adaptive Kalman filtering algorithm, and predicting an abnormal hemodynamic event through a long-short-term memory network. The technical problem of real-time precise detection of the multi-dimensional volume parameters of the blood circulation medical instrument under the complex pulsating flow condition is solved, and accurate evaluation of the instantaneous volume value, the pulsation amount and the pulsating frequency spectrum and early warning of abnormal events are achieved.

The invention discloses a virtual multichannel compressed sensing sampling method and system for partial discharge signals, and belongs to the field of power equipment state monitoring. The problems of high sampling cost, large data volume and low signal reconstruction precision in the existing partial discharge signal sampling method are solved, non-uniform sampling is carried out by adopting a single physical sampling channel, and enough information is obtained at a relatively low sampling rate for signal reconstruction; an observation sequence obtained by sampling is distributed to a plurality of virtual channels according to a time sequence, compressed sensing reconstruction is carried out by using a joint observation matrix matched with a random sampling rate and a virtual channel distribution mode, a complete partial discharge digital signal is recovered at one time, the hardware cost and the system power consumption are effectively reduced, and the system reliability is improved. Therefore, the precision and efficiency of signal reconstruction are improved, and an efficient and reliable solution is provided for state monitoring and fault diagnosis of power equipment.

The invention belongs to the technical field of distributed machine learning, and discloses a hierarchical compression federated learning method and device based on self-adaption and a medium, and the method comprises the steps that a server side selects a client based on a dynamically calculated client participation rate and sparse rate and distributes global model parameters; after local training, the client calculates gradient change, sequentially performs layering, dynamic sparsification and sparse threshold recording, ternary quantization and 1-bit compressed sensing processing, and uploads an obtained compression value, a sparse threshold and training loss to the server; after the server side receives data, on one hand, the participation rate and the sparse rate of the next round are calculated and updated through a self-adaptive participation rate algorithm and a dynamic sparse rate algorithm based on training losses uploaded by all the client sides, and on the other hand, the received compression parameters are subjected to reconstruction and weighted aggregation to update a global model. According to the method, the communication overhead is remarkably reduced, and meanwhile, the model performance almost the same as that of a lossless transmission scheme is kept.

The invention discloses a spectral space coding imaging system and method of a programmable diffractive optical element. The method comprises the following steps: S10, receiving a target scene light signal through a light field model and forming an optical image; s20, performing spatial spectrum joint modulation on the optical image by adopting a programmable diffractive optical element to obtain a coded light field; s30, projecting the coding light field to a detector and obtaining a two-dimensional coding image of primary exposure; and S40, inputting the two-dimensional coding image into a reconstruction module, combining modulation information of the programmable diffractive optical element, and performing decoding reconstruction by using a compressed sensing algorithm to obtain a high-resolution space image and spectral information of the scene. According to the invention, the limitation of the existing system on spatial / spectral resolution is broken through, the system structure is simplified, and the hardware programmability is improved; the imaging capability in a dynamic scene is improved; the signal-to-noise ratio and the penetrating power are improved; and balancing a physical model and a data driving method.

The invention relates to the cross technical field of image processing and artificial intelligence, in particular to an image compression method combining importance prediction and adaptive sampling compressed sensing, which comprises the following steps: acquiring an image data set, and preprocessing to obtain image blocks; constructing an importance prediction network, analyzing the image blocks through the importance prediction network, and outputting an importance map; carrying out adaptive sampling processing on the importance map, outputting the sampling rate of each image block, and constructing a weighted measurement matrix; performing compressed sensing projection on the image data set based on the weighted measurement matrix to generate a low-dimensional measurement value; and inputting the low-dimensional measurement value into a deep reconstruction network for image reconstruction to obtain a reconstructed image. For relatively high reconstruction delay, invalid measurement and invalid calculation are reduced through cooperative matching of a sampling side and a reconstruction side, and reasoning delay is reduced; the storage and transmission cost is remarkably reduced under the conditions of low sampling rate and low computing resources, and meanwhile, the reconstruction quality equivalent to that of a high sampling rate scheme is kept.

The invention discloses a temperature-pressure-resistant vibration methane carbon isotope detection system for oil and gas logging, which belongs to the technical field of oil and gas logging and comprises a gas sample pretreatment module, an optical sensing module, a driving and temperature control module and a core control and algorithm module. The core control module is realized by an FPGA (Field Programmable Gate Array) and is integrated with a self-adaptive multi-resolution scanning and compressed sensing reconstruction algorithm. The method comprises the following steps: performing current segmented scanning and wavelength modulation on the laser; light path anti-vibration coupling is realized through closed-loop control of the MEMS galvanometer; acquiring sparse sampling spectrum data; reconstructing a high-resolution spectrum by using compressed sensing; and inversing CH4 and CH4 concentrations by adopting Voigt linear fitting, and calculating a delta C value. The precision, the speed and the long-term stability of methane carbon isotope detection in a logging site temperature-pressure vibration coupling disturbance environment are remarkably improved.

The invention relates to the technical field of image reconstruction, in particular to a compressed sensing driven terahertz tomography reconstruction method based on an adaptive strategy. According to the technical scheme, the method comprises the following steps that a terahertz continuous wave imaging system is used for collecting intensity data P of a corresponding view angle at equal-interval sparse angles, and a sinogram of a sparse sampling view angle is obtained; performing data preprocessing on the intensity data to unify the data scale; relevant parameters of the algorithm are initialized, wherein the relevant parameters comprise step length and regularization parameters; through a core parameter dynamic adjustment mechanism, cooperative improvement of imaging quality, reconstruction efficiency and environmental adaptability is successfully realized, clearer and more accurate terahertz tomographic images can be obtained, reconstruction can be completed at a faster calculation speed, and the method is suitable for wider practical application scenes and has good application prospects. And the comprehensive performance is obviously superior to that of a traditional non-adaptive method.

The invention discloses an image depth compressed sensing method and system for multilevel privacy protection, and relates to the technical field of image processing and information security, and the method comprises the steps: partitioning an image and a privacy mask to obtain a plurality of image blocks and a plurality of corresponding mask blocks; calculating the privacy weight of the image block based on the mask block, and dynamically allocating the sampling rate of the image block based on the privacy weight; judging a privacy block from the plurality of image blocks according to the privacy weight, and applying reversible encryption disturbance to the privacy block based on the key to obtain a disturbed privacy block; performing compressed sampling on the non-privacy block and the disturbed privacy block according to the sampling rate allocated for each image block to obtain an observation value; and inputting the observation value into a pre-trained double-branch reconstruction network, performing reconstruction through a first branch based on the observation value to obtain a privacy protection image, performing reconstruction through a second branch based on the observation value in combination with a key of a completely authorized user to obtain a high-fidelity image, and realizing image privacy protection and high-quality reconstruction under a deep compressed sensing framework.

An operating method for a LiDAR system, in particular of the compressed sensing type. On the emission side, primary light is emitted in an unstructured manner into a visual field for the illumination thereof, and on the receiving side, light from the visual field is received as secondary light, is converted by light structuring using a predefined, fixed, and temporally constant, matrix-like pattern, into restructured secondary light having at least one matrix-like light pattern consisting of columnar patterns, and for detection, is respectively imaged column by column using the columnar patterns on an associated common detector element of a detector arrangement and detected as a whole.

Aspects of the present disclosure include methods, apparatuses, and computer-readable medium for generating an occupancy vector based on one or more sensor signals captured by one or more sensors configured to detect a presence of one or more objects in an area, wherein each element of the occupancy vector corresponds to a cell in an occupancy grid that divides the area into a number of cells, wherein each element of the occupancy vector has a value indicating that a respective cell of the occupancy grid is occupied responsive to an object being detected in the respective cell by the one or more sensors; applying a sensing matrix to the occupancy vector to generate a compressed occupancy vector that has fewer elements than the occupancy vector; and transmitting, to a network entity, one or more signals indicative of the compressed occupancy vector.

This invention discloses a high-overload data analysis method and system based on multi-dimensional buffer protection, relating to the field of data acquisition and storage technology. The system consists of several functional modules, including: a transient impedance module, which acquires stress wave propagation parameters and material strain data collected in real time by the recorder node, constructs a dynamic digital twin model of the buffer structure, and outputs impact feature vectors and stress field distribution matrices; a signal reconstruction module, which dynamically configures the sensing signals based on the impact feature vectors, including the gain, bandwidth, and filtering parameters of the analog front end, conditions and performs analog-to-digital conversion on the sensing signals, and uses compressed sensing and dictionary learning algorithms to sparsely reconstruct abnormal data, generating a high-fidelity data stream and storage data integrity identifiers for each storage block; and a federated storage module, which inputs the stress field distribution matrix and storage data integrity identifiers into the edge-side reinforcement learning decision engine to calculate the failure risk probability of each storage block.

The application is suitable for the technical field of radar signal processing, and provides a deep unfolding ISAR super-resolution imaging method based on sparse-neighborhood joint constraint, comprising: firstly, constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and the compressed sensing theory, using ADMM to solve the ISAR super-resolution imaging problem, unfolding the process of solving the ISAR super-resolution imaging problem by ADMM into a multi-level neural network, and finally inputting low-resolution ISAR echoes into the trained neural network to obtain an ISAR super-resolution imaging result. The application combines sparse constraint and neighborhood amplitude constraint, deduces a corresponding signal model and an ADMM solving algorithm, effectively improves the reconstruction ability of the algorithm to complex target details and the representation ability of the algorithm to structural information, and can realize effective super-resolution imaging based on narrowband short-aperture echoes.

The application discloses a kind of light field regulation detection method and device based on computing spectral reconstruction, belong to spectral analysis technical field.The method is by introducing medium-metal composite nanostructure, integrates metal nanoisland array and silicon nitride nanocolumn array on quartz substrate, realizes electric field enhancement using the local surface plasmon resonance effect excited by metal nanoisland array, simultaneously realizes wide spectral range phase coding by medium nanocolumn array;Super surface chip is integrated with single point detector, combines pre-calibrated super surface sensing matrix A and enhancement factor matrix E, and reconstructs spectrum using compressed sensing or Tikhonov regularization algorithm.The application keeps the advantages of miniaturization and no moving parts, realizes directional high sensitivity detection to weak signal, and signal-to-noise ratio is improved.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on cross-frame guidance and dual-view interaction enhancement. MRI image pairs are obtained, and image compressed sensing reconstruction is performed by using a network comprising a sampling module, an initial reconstruction module and a plurality of iterative reconstruction modules. Each iterative reconstruction module comprises two branches and a double-view-angle interaction enhancement module, and one branch outputs key frame reconstruction guide information; and the other branch reconstructs the auxiliary frame reconstruction image, and performs adjacent slice joint reconstruction by combining key frame reconstruction guide information. Frequency domain information is reserved by constructing image pairs with different sampling ratios, adjacent slices have high spatial correlation, the cross-frame guide module is used for performing feature interaction and information compensation between the slices with different sampling ratios, the double-view-angle interaction enhancement module is used for enhancing structural continuity from different axial directions, and the structural continuity is enhanced. And the global structure is consistent and details are reserved.

The invention discloses a drill seismic source signal acquisition method and device, equipment and a medium, and relates to the technical field of seismic exploration. The method comprises the following steps: in an underground excitation process of an earthquake while drilling, respectively arranging a first seismic detector and a second seismic detector in a preset range of a drill bit and a preset range of a wellhead so as to obtain a corresponding first seismic wave reference signal and a corresponding second seismic wave reference signal; determining a seismic wavelet signal based on the first seismic wave reference signal and the second seismic wave reference signal, and generating a target seismic record by using the seismic wavelet signal and seismic wave data collected by a third seismic detector arranged on the earth surface; and processing the target seismic record by using a compressed sensing inversion technology to obtain a target drill seismic source signal. According to the technical scheme of the invention, the drill seismic source signal with high resolution and signal-to-noise ratio can be obtained.

The application discloses a semi-implicit neural map construction method based on grid-like cell space coding. The method uses grid-like cell space coding to abstractly encode a three-dimensional space, inputs the coding result into a neural network for decoding, and generates a new visual view through rendering. The grid-like cell space coding on the three-dimensional space can improve the correlation between data, eliminate redundant information in the neural map, realize compression sensing of the environment, maximize the use of information, and thus improve the map reconstruction quality. The semi-implicit neural map construction method can be applied to technical fields such as medical imaging, automatic driving, game development or indoor design, and can automatically generate a three-dimensional scene model according to an input two-dimensional image.

This invention discloses a method for compressing and reconstructing distribution network data based on graph transform compressed sensing, belonging to the field of distribution network measurement data technology. The method includes the following steps: inputting the network topology and historical operating data of the distribution network; constructing a power-voltage relationship parameter matrix; determining the number of principal components and obtaining the projected principal component coefficient matrix; calculating the graph Fourier transform basis; using the graph Fourier transform basis as the sparse basis after projecting the principal component coefficient matrix, calculating the sparsity and initial compression of each principal component under the sparse basis, and obtaining spatially compressed observations; reconstructing the voltage amplitude measurement data of distribution network nodes, and further reconstructing the active power measurement data and reactive power measurement data of branches. This invention employs the above-mentioned method for compressing and reconstructing distribution network data based on graph transform compressed sensing, achieving high data reconstruction accuracy while maintaining a high compression ratio, reducing computational load, and making it more suitable for applications on edge-side devices.

The application discloses a time-frequency electromagnetic exploration method and system for improving exploration depth and resolution, and relates to the technical field of exploration. The method comprises the following steps: arranging a multi-directional excitation source and a mobile remote end array observation system, and sparsely collecting time-frequency electromagnetic signals; reconstructing data and reducing noise based on compression sensing; constructing a time-frequency dual-domain joint inversion target function, introducing cross gradient constraints and physical constraints, and realizing intelligent collaborative inversion through depth reinforcement learning; outputting a high-precision three-dimensional geological model through Bayesian uncertainty analysis and multi-source data fusion verification; and the system comprises excitation, observation, data processing, intelligent inversion and interpretation visualization modules. The scheme improves exploration depth and resolution, improves data acquisition and inversion efficiency, and guarantees the accuracy of geological interpretation.

The application discloses a channel estimation method and device, electronic equipment, storage medium and product, and relates to the technical field of communication. The channel estimation method comprises the following steps: determining the reflection times of each channel link in a communication system through an intelligent reflecting surface; determining effective receiving signals of each channel link in turn according to the reflection times, and determining channel estimation results of each channel link by using a compressed sensing algorithm based on the effective receiving signals; and determining the channel estimation results of the communication system according to the channel estimation results of each channel link. According to the embodiment of the application, the effective receiving signals of each channel link are determined in turn according to the reflection times, and then the channel estimation results of each channel link are determined by using the compressed sensing algorithm based on the effective receiving signals, so that the channel estimation results of the communication system are determined, the channel estimation of any multi-RIS scene is realized, the modeling mode of the channel estimation does not need to be adjusted, and the efficiency of the channel estimation of the communication system is improved.