82 results about "Compressed sampling" patented technology

The invention discloses a distribution line broadband waveform sampling method, and belongs to the technical field of power system monitoring. By collecting quantum vacuum fluctuation noise, temperature, humidity and electromagnetic parameters, a quantum environment coupling entropy model is constructed, an optimal sparse base dictionary is dynamically generated, and a noise entropy break variable is calculated. And when the entropy break variable exceeds a threshold value, triggering sub-Nyquist compressed sampling, otherwise, adopting Nyquist rate sampling to realize dynamic matching of sampling resources and signal complexity. A high-frequency component of a compressed signal is reconstructed by combining an adversarial compressed sensing network with a residual network, a low-frequency component is generated based on an original signal, the distribution consistency of high and low frequency entropies is optimized by using a Wasserstein distance, and finally a three-dimensional waveform matrix containing a timestamp, a frequency bin and quantum entropy confidence is output. The problems of low sampling efficiency, poor sparse representation adaptability, slow noise response, inconsistent reconstruction and the like in a non-stationary signal and complex noise environment are solved, and the precision and energy efficiency of broadband waveform sampling are remarkably improved.

The invention relates to a multi-dimensional transmission data compression sensing method and system for urban emergency rescue, and the method comprises the steps: obtaining multi-modal data, carrying out the normalization processing and sparse representation, and obtaining the sparse coefficient of each modal; constructing a block diagonal random matrix for each modal sparse coefficient to perform independent compression sampling, generating a low-dimensional observation value before coding, and performing arithmetic coding and low-density parity check code error correction coding to generate an anti-interference code stream; and performing low-density parity check code decoding and arithmetic decoding on the anti-interference code stream to obtain a decoded low-dimensional observation value, performing iteration by using an orthogonal matching pursuit algorithm, recovering sparse coefficients of each mode, performing reconstruction to obtain text, image and audio features, and projecting the text, image and audio features to a shared semantic space to obtain a shared semantic space. And calculating a joint semantic weight through a cross-modal attention mechanism, and generating a multi-modal fusion result with consistent semantics. The method disclosed by the invention provides an efficient, robust and semantic collaborative end-to-end solution for urban emergency rescue.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention discloses an ultra-harmonic rapid detection method based on compressed sampling and multiple measurement vectors, and solves the problems of high sampling rate, low reconstruction efficiency and insufficient spectrum resolution in the prior art. Sub-Nyquist compressed sampling is realized through a sparse random matrix, and the hardware burden is reduced; a dynamic step size sparse self-adaptive matching pursuit algorithm (DSSAMP) is adopted to reconstruct signals, the step size is adjusted in a self-adaptive mode in combination with residual energy changes, and the convergence speed and precision are balanced; an improved multi-measurement vector model (IMMV) is constructed through framing processing and introduction of interpolation factors and prior frequency point screening, and the calculation efficiency is improved; and finally, extracting super-harmonic frequency, amplitude and phase parameters based on an orthogonal matching pursuit (OMP) algorithm. According to the method, the high resolution is ensured, the sampling rate and the calculation complexity are remarkably reduced, and the real-time requirement is met.

The invention relates to the technical field of unmanned aerial vehicle signal transmission, and discloses an unmanned aerial vehicle signal transmission rate detection method and system.The method comprises the steps that a compressed sampling matrix is constructed through quantum random projection to reduce the signal dimension, and a quantum entanglement gate is driven based on attitude parameters to generate a dynamic sparse basis matrix; a federated learning framework is adopted to be fused with multi-node data optimization base parameters, rate detection is converted into combinatorial optimization solution in combination with a quantum annealing algorithm, and finally real-time adjustment of a modulation mode and transmitting power is achieved through closed-loop control. The innovation is embodied in a quantum compression sampling-federated optimization-annealing detection cross-domain cooperation mechanism, the defects of a traditional method in dynamic adaptability, calculation efficiency and anti-interference performance are overcome, and the method has the technical advantages of sparse representation space dynamic adaptation, quantum parallel acceleration optimization, multi-target parameter balance and the like. And the communication rate detection precision and link reliability of the unmanned aerial vehicle in a complex electromagnetic environment are remarkably improved.

The invention discloses a direct current charging pile detection system and method applied to the field, and relates to the technical field of power equipment on-line monitoring, and the method comprises the steps: inputting a three-dimensional data set into a Toeplitz cyclic measurement matrix for compressed sampling, reconstructing the three-dimensional data set into a high-resolution signal through employing an improved orthogonal matching pursuit algorithm, and carrying out the detection of the high-resolution signal; analyzing and calculating a local variable coefficient through a sliding window, marking abnormal positions, and integrating the abnormal positions into an abnormal position set; inputting the abnormal position set into a pre-trained long and short term memory prediction model, evaluating the insulation residual life and the fault risk level, and calculating the maximum allowable output current of the DC charging pile in real time; the load impedance is dynamically adjusted and detected through the memristor array, the output current of the direct current charging pile is collected in real time, and synchronous compression wavelet transform is adopted to analyze a harmonic ridge line and extract a harmonic component; the accuracy of fault early warning is remarkably improved, rapid dynamic adjustment of the output current is achieved, and the technical problem of multi-physics field coupling monitoring of the rapid charging pile is effectively solved.

The invention provides a data compression sampling method and device, and the method comprises the steps: obtaining an original collection signal through data collection equipment, and storing the original collection signal to a target region; processing the original acquisition signal in the target area by using a deterministic measurement matrix and a sparse transformation matrix in response to the condition that the data volume of the target area reaches a preset value to obtain a processing result; and transmitting the processing result to target equipment. Through the deterministic measurement matrix and the sparse transformation matrix, linear mapping and instant data stream compression are carried out on the sampled original acquisition signals, the data transmission amount is significantly reduced, real-time signal processing and interaction in a resource-constrained environment are supported, and the method has small limitation on a hardware environment, has good transportability and deployment flexibility, and is suitable for large-scale popularization and application. The method is suitable for various system application requirements, so that efficient data compression sampling is realized.

The invention discloses a pulse terahertz time-domain spectroscopy system of a single photoconductive antenna and a detection method, and relates to the technical field of terahertz, the system comprises a laser pulse generation module, a single photoconductive antenna module, a compression sampling module, a signal reconstruction module and a control unit; by integrating the random light delay modulation unit and the single-pixel detector, the system realizes random time interval sampling of terahertz signals, effectively breaks through the limitation of a traditional sampling mode, improves the flexibility and efficiency of signal acquisition, and combines a signal reconstruction module of an FPGA + GPU heterogeneous computing architecture, so that the system has a wide application prospect. The system can adaptively construct a sparse base and perform accurate sparse representation on a measurement vector, so that an original terahertz time domain signal is efficiently reconstructed by optimizing a target function and an adaptive iterative solution algorithm, the signal reconstruction precision is improved, the data processing complexity is also reduced, and the method is suitable for large-scale popularization and application. And powerful support is provided for practical application of the terahertz technology.

The invention discloses an X-ray diffraction spectrum sparse reconstruction method and system based on a dynamic dictionary, and the method comprises the steps: generating an initial dictionary, and carrying out the dynamic optimization and updating of the dictionary, and obtaining a sparse dictionary; selecting an X-ray diffraction scanning angle subset based on the sparse dictionary, constructing an observation matrix, encoding an observation process, and obtaining compressed and sampled X-ray diffraction projection data; and sparse vectors are obtained from the obtained X-ray diffraction projection data after compressed sampling, and a reconstruction spectrum is obtained. And the accuracy of sparse representation is improved. The invention provides a high-throughput XRD (X-Ray Diffraction) material spectrogram analysis method combining dynamic dictionary learning, a compressed sensing theory and deep learning, and aims to improve the analysis efficiency and precision of crystal structure recognition and phase composition in new material research and development.

The invention relates to a frequency hopping signal detection method and system based on time-frequency compressed sampling deep learning, belongs to the field of signal processing, and solves the problems of low detection probability and high calculation complexity of a frequency hopping signal under a low signal-to-noise ratio. Comprising the following steps: respectively acquiring a frequency hopping signal when the frequency hopping signal exists and noise data when the frequency hopping signal does not exist, and carrying out time-frequency characterization and compressed sampling processing to obtain a frequency hopping time-frequency graph and a noise time-frequency graph; constructing a sample data set based on the frequency hopping time-frequency graph, the noise time-frequency graph and corresponding sample labels; constructing a frequency hopping signal detection network based on a lightweight convolutional neural network ShuffleNet and a convolutional attention module CBAM, and obtaining a trained frequency hopping signal detection network by using the sample data set; acquiring a time-frequency graph corresponding to a to-be-detected signal, and inputting the time-frequency graph into the trained frequency hopping signal detection network to obtain a confidence coefficient of the to-be-detected signal; and performing judgment based on the confidence of the to-be-detected signal to obtain a detection result of the to-be-detected signal. The detection performance is improved; and the reasoning complexity is reduced.

The invention belongs to the technical field of communication, and discloses a communication method and system based on broadband interference suppression and target signal reconstruction, and the method comprises the steps: collecting an original broadband communication signal; carrying out compressed sampling on the preprocessed signal to obtain compressed data; performing time-frequency domain conversion on the compressed data, and performing sparse representation on the signal by using the sparse dictionary to obtain time-frequency domain conversion data; reconstructing an original broadband communication signal from the time-frequency domain conversion data by adopting an iterative reconstruction algorithm and combining an interference suppression strategy, and eliminating an interference component to obtain a target signal; and comparing the quality index of the reconstructed target signal with a preset performance index, and dynamically optimizing at least one key parameter of compressed sampling, sparse representation, iterative reconstruction or interference suppression according to a comparison result. According to the method, a complex and changeable interference environment can be adaptively dealt with, it is ensured that the system always keeps the optimal processing effect, and the robustness is far better than that of a traditional method adopting fixed parameters.

The invention provides a DOA (direction of arrival) estimation method and device of a random linear array. The DOA estimation method comprises the following steps: establishing an observation model representing a relationship between direct output of a sensor unit and a source signal, and carrying out compressed sampling on the direct output of each unit of the linear array by applying a random demodulator; constructing a linear observation relationship between the sampling signal of the random demodulator and a decomposition coefficient matrix of the source signal matrix on a certain sparse base, obtaining an estimated value of the decomposition coefficient matrix by using a sparse sampling reconstruction method, and further reconstructing to obtain an estimated value of an original source signal matrix; and calculating the DOA spectrum and estimating the DOA angle of the target or source according to the peak value of the spectrum. According to the invention, the technical problems of low estimation precision and low resolution caused by large design difficulty of a sensor array, heavy data acquisition burden and single analysis signal are solved.

The invention discloses a broadband spectrum reconstruction method and system for low-orbit satellite signals, and the method comprises the steps: S1, carrying out the sampling of the low-orbit satellite signals through multi-coset compression sampling, and obtaining the sampling data; s2, performing fast Fourier transform on the sampled data to obtain a measured value matrix; and S3, reconstructing the input signal # imgabs2 # according to the measured value matrix # imgabs0 # and the sensing matrix # imgabs1 # by adopting a neural compressed sensing method, and taking the reconstructed input signal # imgabs3 # as the broadband frequency spectrum of the low-orbit satellite signal. According to the method, more accurate spectrum reconstruction can be realized; and the method also has relatively high performance in a scene with limited samples. According to the method, multi-coset compression sampling is adopted, so that the cost of signal acquisition equipment can be reduced.

The invention provides a power distribution network node harmonic exceeding main cause identification method, device and system and a medium, and the method comprises the steps: building a node admittance matrix based on the node harmonic voltage and branch current monitoring data of a power distribution network, building a related equation of the harmonic voltage and an injection current, and employing a compressed sampling matching pursuit algorithm to identify the harmonic exceeding main cause of the power distribution network; solving a sparse harmonic injection current vector, and calculating a node harmonic voltage distortion rate according to a harmonic current injection value so as to judge a standard exceeding condition; through a modal analysis method, harmonic current threshold comparison and multi-frequency superposition effect analysis, identifying main factors exceeding the standard, including harmonic injection standard exceeding, multi-frequency superposition and resonance phenomena; and quantizing contribution degrees of resonance and harmonic injection, and distinguishing composite causes of harmonic exceeding. According to the method, the problem that the harmonic cause is unknown in the harmonic exceeding scene is solved, and the accuracy of the harmonic exceeding cause is remarkably improved. Through simulation and actual measurement verification, the accuracy of the method provided by the invention is verified.

A data compression and transmission method for a wireless vibration sensor comprises the following steps: A, initializing acquisition parameters and generating a pseudo random number to realize vibration data compression sampling; b, transmitting the compressed data to a data terminal in multiple batches according to a set mode by adopting a lightweight communication protocol; c, constructing an adjustable measurement matrix according to the data received by the data terminal, and restoring a complete vibration data sequence by using a compressed sensing algorithm; d, the sensor is installed in an actual engineering environment for testing, and compression sampling and transmission parameters are determined; and E, adjusting compression sampling transmission parameters to realize long-term vibration data compression sampling and transmission. On the premise of guaranteeing the integrity and reliability of vibration monitoring data, the energy consumption and communication bandwidth requirements are remarkably reduced, the robustness of wireless transmission is effectively improved, and the service life of wireless transmission equipment is prolonged.

The invention discloses a federal compression learning method and system based on knowledge distillation, and the method comprises the steps: enabling each terminal device to adaptively adjust the compression ratio of the terminal device according to a real-time network, and carrying out the compression sampling through the adjusted compression ratio; then, taking a compressed sampling value as a knowledge distillation medium between edge equipment and a cloud server of different levels, and carrying out cooperative training on heterogeneous models deployed by nodes of different levels in combination with a federated learning method and knowledge distillation; the knowledge distillation is introduced in the federated learning process, so that models with different capacities and architectures can be deployed at different hierarchy nodes, and the compressed sampling value with the internal privacy protection characteristic is used for replacing original data or proxy data as a distillation medium, so that the privacy risk caused by the knowledge distillation is eliminated from the data source. And the communication efficiency, privacy protection and computing power utilization optimization of the end-side cloud are realized.

The application discloses a battery capacity estimation method and system based on adaptive inflection point driving compressed sampling, relates to the technical field of lithium battery capacity estimation, acquires original charging data of a battery to be measured, obtains a voltage curve based on voltage data in the original charging data, identifies an inflection point of the voltage curve, divides the voltage curve into different sampling sections according to the inflection point position, performs segmented random sampling in each section based on a sampling ratio, forms an initial sampling set, introduces a dynamic updating function to iteratively update the sampling ratio according to section information entropy and prediction error feedback under the condition that the total number of sampling points is constant, and realizes adaptive compressed sampling; and the sampling set is input into a capacity prediction model for processing to obtain a predicted battery estimated capacity. The adaptive inflection point guided compressed sampling and the deep learning model adapted to sparse data in structure are combined to realize lightweight and data-efficient battery state estimation.

The present application relates to the technical field of sonar imaging, and particularly relates to a multi-beam sonar compressed imaging method and system based on depth unfolding, which comprises the following steps: a multi-beam sonar system is used to emit sound waves to the seabed, and a receiving device is used to receive reflected echo signals to obtain multi-channel digital time sequence echo signals; an observation matrix is constructed to compress and sample the multi-channel digital time sequence echo signals to obtain a low-dimensional measurement matrix; the low-dimensional measurement matrix is input into a trained depth unfolding beamforming network to output a reconstructed beamforming spectrum, and a sonar sounding image is generated according to the beamforming spectrum. The present application can greatly improve the imaging speed and support real-time applications such as underwater obstacle avoidance and online monitoring.

The embodiment of the invention relates to the technical field of image compressed sensing, in particular to an image compressed sensing method and device for deep expansion of dense multi-scale interaction. A specific embodiment of the method comprises the following steps: carrying out block compression sampling processing on an obtained original image to generate a sampling value; determining an initial reconstructed image; determining the number of initial iterations; based on the initial iteration times and the initial reconstruction image, executing the following iteration steps: determining a gradient descent result; determining a scale original decoder feature; determining scale decoder features based on a preset feature formula and the scale original decoder features; determining a stage reconstruction image based on a preset stage reconstruction formula, the gradient descent result and the scale decoder features; and in response to determining that the number of initial iterations meets a preset iteration condition, determining the stage reconstruction image as a target reconstruction image. According to the embodiment, the image reconstruction quality in image compressed sensing can be improved.

The invention discloses a two-dimensional wave field reconstruction method based on compressed sampling, and belongs to the technical field of signal processing and sensors. According to the method, original time domain signals of a two-dimensional wave field are expressed as a three-dimensional matrix, random sampling is carried out through a Bernoulli distribution random sampling matrix, after the three-dimensional matrix is reduced into a two-dimensional matrix, a group sparse perception convolutional neural network is constructed to serve as a non-convex optimization solver, a real part and an imaginary part of a complex signal are processed, group sparse constraint is applied, and a non-convex optimization solver is constructed. And reconstructing the basis coefficient matrix to obtain an original signal reconstruction result, and finally raising the dimension to restore the original signal into a three-dimensional wave field. According to the method, the sampling rate is reduced, the reconstruction precision is improved, large-scale data set training is not needed, each training is an independent reconstruction process, and the method is suitable for the fields of acoustic imaging, seismic exploration and the like.

The invention discloses a vibration system operation modal parameter estimation method based on compressed random subspace identification, and the method comprises the steps: carrying out the compressed sampling of a monitoring signal of a vibration system through employing a general covariance sampling mode, and obtaining a signal sample; determining a sampling matrix and a sensing matrix based on the general covariance sampling mode; estimating a compressed covariance matrix by using the signal sample; reconstructing a complete covariance matrix based on the covariance matrix; and performing operation modal parameter estimation by using a covariance random subspace identification algorithm.

A power spectrum estimation method for compressed sampling of a generalized stationary HDR signal is disclosed. This method is used for processing aero-engine blade tip vibration monitoring signals. The method involves determining the threshold of a self-recovering analog-to-digital converter and the delay scheme for multi-channel compressed sampling based on prior information about the signal amplitude and frequency; compressing the signal using a multi-channel dual-threshold cooperative sampling method; recovering the correct amplitude information from the acquired signal using the dual-threshold information; constructing an indicator sequence and a data sequence based on the sampling delay scheme; and performing Fourier transform, element-square operation, inverse Fourier transform, and element-product calculation sequentially on the indicator sequence and data sequence to obtain the power spectrum estimate of the signal.

The invention discloses a two-dimensional wave field reconstruction method, device and terminal based on spatial sparse sampling, and the method comprises the steps: obtaining an original two-dimensional time domain signal corresponding to an original signal, carrying out the compression sampling of the original two-dimensional time domain signal, and determining a two-dimensional discrete time domain signal; fourier transform is carried out on the two-dimensional discrete time domain signal, and a two-dimensional discrete frequency domain signal is determined; solving a two-dimensional wave number signal according to the two-dimensional discrete frequency domain signal based on a compressed sensing theory; and performing inverse Fourier transform on the two-dimensional wave number signal, determining a reconstructed two-dimensional time domain signal, and determining a reconstructed signal corresponding to the original signal according to the reconstructed two-dimensional time domain signal. According to the invention, the two-dimensional wave number signal is reconstructed according to the two-dimensional discrete frequency domain signal obtained by sampling through compressed sensing, and the reconstructed signal corresponding to the original signal is obtained according to the two-dimensional wave number signal, so that the problem that the two-dimensional wave field data is sampled by adopting Nyquist in the prior art can be effectively solved; the data redundancy is high; and the transmission pressure is large.

The invention discloses a compressed sensing-based communication sensing integrated channel estimation reconstruction method, which comprises the following steps of: cleaning, de-noising and normalizing original data from different modes to ensure the quality and consistency of the data; extracting features useful for channel estimation from data of different modes by using a feature extraction algorithm, and fusing the features from the different modes by using a fusion algorithm to form comprehensive description of a channel; and channel estimation is realized by using a compressed sensing theory. According to the method, the complementarity and redundancy of the multi-modal data are utilized, an efficient compressed sampling strategy is designed, and comprehensive description of channel characteristics is achieved; meanwhile, an adaptive channel reconstruction algorithm is provided, reconstruction parameters and strategies are dynamically adjusted according to channel characteristics and data quality, and the reconstruction performance and accuracy of the algorithm in complex and changeable scenes are improved.

This invention discloses a signal enhancement method and apparatus for time-slice compressed sensing 3D imaging radar. First, an M×N dimensional random binary matrix is ​​generated as the measurement matrix for compressed sampling. The target area is illuminated using a laser source, and repeated sampling and modulation are performed to obtain a sampling matrix. The j-th column of the sampling matrix is ​​extracted to generate a compressed sampling vector for the corresponding time slice. The covariance between the compressed sampling vector and each column of the measurement matrix is ​​calculated to obtain the compressed sampling covariance vector. The covariance between each column of the measurement matrix and all columns is calculated to obtain the covariance measurement matrix. Based on a compressed sensing 2D reconstruction algorithm, the 2D image of each time slice is reconstructed using the covariance measurement matrix. The reconstructed 2D images of all time slices are stitched together to form a complete 3D image. This method and apparatus can solve the problems of low reconstruction accuracy and poor image quality in existing compressed sensing 3D imaging technologies under noisy environments.

This invention discloses a short-time compressed sampling method and system based on a modulation wideband converter (MWT) to solve the problem of real-time acquisition of short-time burst wideband sparse signals. The method reduces the compression and reconstruction unit of the MWT to multiple short-time segments of the uncovered signal's complete time domain. For each segment, a short-time reconstruction algorithm is used for independent reconstruction. This algorithm integrates principal component analysis for dimensionality reduction, a modified atom matching strategy for selecting conjugate atom pairs, recursive prospect orthogonal matching pursuit for determining the support set, and time-domain support extraction to suppress noise. Finally, the complete signal is recovered through segment reconstruction. This invention significantly reduces reconstruction complexity, improving the signal-to-noise ratio of reconstructed 5GHz wideband signals at a 2.5GHz sampling rate by more than 4dB compared to traditional methods, and exhibits stronger robustness to changes in sparsity and channel number, achieving low-power, high-precision sub-Nyquist sampling.

The invention discloses a modulated broadband converter joint blind calibration method for a sparseness unknown scene, and belongs to the technical field of compressed sampling. The problems that a traditional calibration method is high in implementation process complexity and calibration cost, and the number of input signal sources needs to be predicted are solved. According to the method disclosed by the invention, the signal sparseness can be estimated by only modulating the sampling data actually output by the broadband converter during compressed sampling and combining an information theory criterion, so that synchronous solution of system joint blind calibration and signal reconstruction is realized, and meanwhile, system error calibration and accurate recovery of an original signal are completed. According to the method, a traditional active calibration method needing test excitation is not used for the calibration problem of the modulation broadband converter any more, and the system can be calibrated and the signal can be reconstructed under the condition that the sparseness is unknown, so that the implementation process is simple and convenient, and the calibration cost is greatly reduced. The method can be applied to joint blind calibration of the modulation broadband converter.

The application discloses a single light guide antenna pulse terahertz time-domain spectroscopy system and a detection method, and relates to the technical field of terahertz. The system comprises a laser pulse generation module, a single light guide antenna module, a compressed sampling module, a signal reconstruction module and a control unit. The application integrates a random light delay modulation unit and a single-pixel detector, realizes random time interval sampling of a terahertz signal, effectively breaks through the limitation of a traditional sampling mode, improves the flexibility and efficiency of signal acquisition, and combines a signal reconstruction module of an FPGA+GPU heterogeneous computing architecture. The system can adaptively construct a sparse base, accurately sparsely represent a measurement vector, and then efficiently reconstruct an original terahertz time-domain signal through an optimization objective function and an adaptive iterative solution algorithm. The application not only improves the accuracy of signal reconstruction, but also reduces the complexity of data processing, and provides strong support for the practical application of terahertz technology.

The invention discloses an inverse synthetic aperture radar imaging method and system with phase distortion and two-dimensional compression sampling, terminal equipment and a medium, and relates to the technical field of radar imaging. The method comprises the following steps: constructing a sparse recovery model containing phase error correction based on a low-rank and sparsity principle; wherein the sparse recovery model is adapted to an inverse synthetic aperture radar echo signal which is subjected to two-dimensional compression sampling and has phase distortion; and based on a unified alternating direction multiplier method framework, solving the sparse recovery model to complete sparse recovery and phase error correction of the two-dimensional compressed sampling signal, and outputting an inverse synthetic aperture radar image. According to the method, the problems that phase errors are ignored in existing inverse synthetic aperture radar imaging and phase correction fails during down-sampling are solved, a clear image can be generated, and the calculation complexity is reduced.

Examples of this topic provide techniques for compressed sampling of acoustic data. The probe can sample in a compressed mode, so that the entire matrix is ​​not sampled at full temporal or spatial resolution. Thus, the amount of initial data captured by the probe is reduced, enabling the use of lower-density hardware (e.g., fewer analog-to-digital conversion channels or related analog front-end hardware) at lower data rates.