46 results about "Matching pursuit" patented technology

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) remote sensing imaging, in particular to a scattering heuristic SAR tomography method for structured three-dimensional reconstruction, which comprises the following steps of: S1, acquiring and preprocessing multi-baseline SAR data; s2, inverting scattering parameters based on an attribute scattering center model and orthogonal matching pursuit; s3, segmenting a building area according to coherence and morphological indication; s4, combining direction perception expansion with region updating, determining a continuous scattering region, and giving reference height prior; and S5, constructing a regional adaptive dictionary, sparsely solving the elevation and geocoding to generate a structured point cloud. According to the method, physical prior and regional statistics are cooperated, geometric continuity and anti-noise robustness are improved, pseudo peaks and calculation redundancy are reduced, and upgrading from sparse point cloud to a structured model is realized.

The invention relates to the field of rotor blade non-contact state monitoring, in particular to a blade tip timing vibration measurement signal parameter identification method and system under a time-varying rotating speed. Comprising the following steps: constructing a matrix Chirp signal model; performing initial frequency pre-analysis based on a block orthogonal matching pursuit method to obtain an estimated value; defining a range interval of initial frequency analysis and an analysis range of a frequency modulation slope by taking the estimated value as a center, and obtaining an orthogonal matching pursuit result based on a matrix Chirp signal model and a block orthogonal matching pursuit method; and according to a matching tracking result, calculating to obtain a vibration amplitude. According to the method, the frequency modulation slope is introduced to serve as an additional parameter, the change trend of the frequency along with time is reflected, the vibration rule of the blade under the centrifugal stiffening effect can be more truly described, and an accurate physical basis is provided for analysis of non-stationary signals.

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.

The application provides an active noise reduction method and system for road noise and wind noise of an automobile, and the active noise reduction method comprises the following steps: S1, collecting a dynamic parameter signal and a total mixed noise signal during automobile driving; S2, constructing a dynamic calculation model of a Strouhal number according to a Reynolds number, deducing a real-time calculation model of a characteristic frequency of each noise source i according to the dynamic calculation model of the Strouhal number, and outputting a real-time characteristic frequency sequence corresponding to each noise source i; S3, constructing a global signal dictionary D, using an orthogonal matching pursuit sparse representation algorithm, and separating a single noise source signal in the total mixed noise signal by iteratively screening a dictionary atom with the highest matching degree with the characteristic frequency; S4, using a frequency tracking type fast adaptive filtering algorithm to generate a corresponding anti-phase cancellation signal; and S5, outputting the anti-phase cancellation signal in the automobile cabin, so that noise reduction can be performed on each noise source i.

The invention belongs to the technical field of time-frequency spectrum decomposition, and discloses a time-frequency spectrum decomposition method and device based on empirical matching pursuit and a storage medium, the time-frequency spectrum decomposition method comprises the following steps: constructing an empirical mode decomposition (EMD) dictionary suitable for time-frequency analysis according to input seismic data; performing matching pursuit on a seismic signal to be decomposed by using the EMD dictionary to obtain a time-frequency atom combination of the seismic signal; and performing multiple synchronous compression transformation on the time-frequency atoms of the seismic signal to obtain the time-frequency distribution of the seismic signal. According to the method, a new time-frequency characterization method is deduced, a time-frequency spectrum decomposition strategy based on empirical matching pursuit is designed, and the time-frequency resolution of seismic signals is improved by introducing multiple synchronous compression transformation.

The invention belongs to the technical field of data processing and analysis, and particularly relates to a carbonate rock thermal storage acidizing fracturing permeability increase rate inversion method. Comprising the following steps: step 1, installing an optical fiber distributed sound wave monitoring and optical fiber distributed temperature monitoring composite cable at a target well section to collect monitoring data; 2, processing the collected monitoring data, and constructing a unified observation sequence; 3, a combined response template library corresponding to the established well periphery modeling unit is constructed, and orthogonal matching pursuit type strip selection and residual error updating are executed based on the compressed sensing thought by taking the constructed unified observation sequence as a target so as to generate a sparse permeability increasing rate field; and 4, performing numerical checking on the generated sparse permeability increasing rate field, and outputting the corresponding sparse permeability increasing rate field. According to the technical scheme, the calculation efficiency and adaptability are remarkably improved while the result reliability is guaranteed.

The invention belongs to the technical field of underwater acoustic communication, and relates to a matching pursuit decision feedback equalization method, device and equipment and a storage medium, and the method comprises the steps: initializing system parameters; constructing a training data matrix, and reorganizing the receiving signals of the time sequence and the known training symbols into a matrix form; global information required in the iteration process is pre-calculated and stored according to the training data matrix; carrying out rapid matching pursuit iteration solution, gradually selecting an atom, namely an equalizer tap, which is most related to the current residual error from the dictionary in an iteration mode, and updating the coefficient and the residual error until sparseness constraint is met; mapping a solution obtained on the normalized augmented system back to an original physical equalizer coefficient, and initializing the state of the equalizer; and performing continuous equalization and demodulation on unknown data symbols by using the filter coefficient obtained in the training stage, and recursively eliminating inter-symbol interference. And the stability and the overall performance of the underwater acoustic communication receiver under a complex channel condition are remarkably improved.

This invention proposes a phase ranging method based on orthogonal matched pursuit (OMP) and three-point interpolation refinement, applicable to BLE / SLE narrowband wireless communication systems. The method first constructs a delay-frequency dictionary, obtains multi-channel phase vectors through bidirectional phase measurement, and then expands and normalizes them. Subsequently, the OMP algorithm selects the atom that best matches the measured phase from a dynamically generated predefined dictionary, obtaining a coarse delay estimate. Then, based on the matching amplitudes of the selected atom and its two adjacent points, a three-point parabolic interpolation formula is used to refine the delay using a sub-step size, obtaining a more accurate delay estimate. Finally, the refined delay is converted into a distance value. This method effectively suppresses multipath interference through sparse modeling and overcomes the dictionary step size limit through the interpolation step, significantly improving ranging accuracy and possessing good embedded deployment value.

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.

This invention discloses a sonar signal bandwidth extension method and system based on an adaptive sparse reconstruction approach. It acquires frequency domain data of active sonar echoes through matched filtering and extracts the transmission bandwidth range of the active sonar. A physical perception dictionary containing the physical ripple characteristics of the transmitted waveform is constructed to establish a precise mapping from the sparse target space to the observation frequency band. An adaptive threshold detection mechanism is introduced during the orthogonal matched pursuit iteration process to screen potential targets using the statistical characteristics of local background noise. A meshless global optimization algorithm is used to jointly fine-tune the target position in the continuous time domain. A full-band skeleton spectrum is constructed using precise parameters, and the noise residuals in the original frequency band are back-injected and inversely transformed to obtain a high-resolution time-domain waveform. This invention significantly improves the ability to distinguish closely spaced targets and the detection performance of weak targets in complex underwater acoustic environments.

The invention provides a vehicle-mounted navigation system fault monitoring method, which comprises the following steps of: 1, receiving a signal by using a GNSS receiver and an IMU device in a vehicle-mounted navigation system, positioning by adopting a Kalman filtering method, and obtaining measurement domain information at the same time; step 2, constructing state domain information and a covariance matrix thereof according to the Kalman filtering parameters and the measurement domain information; step 3, separating and judging GNSS faults from the state domain information and the covariance matrix thereof by using a projection operator; step 4, positioning the judged GNSS fault by adopting an orthogonal matching pursuit method and removing the fault, if all satellites are removed, warning that the GNSS is unavailable, otherwise, executing step 5; step 5, according to the state domain information and the covariance matrix of the state domain information after the GNSS fault is eliminated, separating and judging an IMU fault by using a projection operator; and step 6, correcting the IMU fault, and completing fault monitoring of the vehicle-mounted navigation system.

The application belongs to the technical field of wireless positioning and specifically relates to a reconfigurable intelligent surface assisted SISO system user near field positioning method. Regular multivariate decomposition and orthogonal matching pursuit are combined to obtain initial values of TOA and AOD on the RIS, then, a l1-norm regularization method based on sparsity is used to estimate the distance. Then, the maximum likelihood estimation formula is used to refine the initial estimation. Finally, the extended invariance principle is combined with the least square method to recover the position and clock offset. The obtained parameter estimation performance is close to the Cramer-Rao lower bound of the estimation error.

The present application belongs to the field of array signal processing, and relates to a switched antenna array DOA estimation method based on orthogonal matching pursuit joint tracking. In view of the problem of algorithm performance distribution imbalance in traditional switched antenna array DOA estimation, a switched antenna array DOA estimation method based on orthogonal matching pursuit joint tracking is provided. The present application takes a sparse reconstruction algorithm as a starting point, converts the DOA estimation problem into a sparse representation problem of a dynamic dictionary by constructing a spatially over-complete dictionary, utilizes the characteristic that the sparse solutions of various observation values correspond to the same support set, and jointly solves all sampling data by using an orthogonal matching pursuit joint tracking algorithm, so that the structural characteristics of the switched antenna array are fully utilized, the DOA estimation precision is improved, and high-precision and low-complexity switched antenna array DOA estimation is realized.

The application relates to the technical field of relays, and discloses a relay contact action time analysis method based on target matching tracking, which is used for improving the accuracy when analyzing the relay contact action time. The method comprises the following steps: performing power-on testing on a target relay, collecting contact images of the target relay in the power-on testing process, and obtaining a contact image set; pre-processing the contact image set to obtain a processed image set; performing feature recognition on each processed image in the processed image set to obtain contact position information in each processed image in the processed image set; and performing contact action time analysis on the target relay based on the contact position information in each processed image in the processed image set to determine a target contact action time.

The application discloses a low-complexity millimeter wave massive MIMO channel estimation method, comprising the following steps: configuring a plurality of antennas on a receiving end and a transmitting end respectively to construct a MIMO model; modeling a channel matrix as a guided vector superposition to obtain an angle domain sparse parameterization model according to the scattering characteristics of the millimeter wave channel; constructing two low-dimensional dictionaries based on the angle domain parameterization model; constructing a multi-dimensional measurement tensor, equivalently rewriting sparse representation, and transforming a problem to obtain a multi-dimensional compressed sensing mathematical description based on the MIMO model and the two low-dimensional dictionaries; obtaining the initial value of a discrete angle of arrival and a discrete angle of departure and the corresponding gain of each path by multi-dimensional orthogonal matching pursuit based on the multi-dimensional measurement tensor, the two low-dimensional dictionaries and a preset path number; and obtaining continuous angles and a reconstructed channel matrix by constructing a projection residual loss function and adopting an alternating gradient descent optimization based on the initial value of the discrete angle of arrival and the discrete angle of departure, observation data and a measurement matrix.

The application provides a communication and sensing integrated system sensing assisted beam forming method, and relates to the technical field of communication; the method comprises the following steps: S10, an ISAC base station sends a reference signal, the reference signal comprises a reference signal sent omnidirectionally in an initialization stage and a reference signal sent directionally according to a preset encoding vector f k in a sensing assistance stage; S20, an ISAC receiving end utilizes a return signal to acquire parameters of surrounding targets or users based on orthogonal matching pursuit (OMP) and multiple signal classification (MUSIC) respectively; S30, the distance and angle of the targets or users are obtained by using a sensing time slot, and the encoding vector f k is calculated; S40, steps S20 and S30 are repeated to realize sensing assisted cellular communication beam forming; the application has the beneficial effects that high-precision position information of the ISAC receiving end can be sensed, and more accurate sending beams can be formed.

The application relates to an air target identification template library reconstruction method, device and equipment and a storage medium. The method combines an attribute scattering center model prior and derivation of a scattering coefficient amplitude and azimuth angle relationship under a physical optical method, constructs a scattering center evolution model reflecting a coupling relationship between a target scattering center and an azimuth angle, realizes model parameter estimation based on Newton orthogonal matching pursuit and simplex method, and finally reconstructs a target identification template library based on the SCEM. The method can realize accurate and stable identification template library reconstruction under the condition of incomplete ISAR image samples and does not depend on the number of samples.

The embodiment of the invention provides a generative model content control method and device, equipment and a storage medium, relates to the technical field of visual models, and is suitable for financial science and technology and medical health scenes. According to the method, a semantic dictionary is defined according to a de-embedding matrix of a generative model, specialization scores of attention heads in the generative model to sub-dictionaries in the semantic dictionary are calculated by adopting a greedy algorithm of synchronous orthogonal matching pursuit, and then key heads are selected according to the specialization scores. And then obtaining directional editing information and an output signal of the key head, and performing weighting coefficient calculation on the output signal by using the directional editing information, so that the generative model outputs a directional editing result. According to the method, the functions of the specific components in the generative model can be quantified through sparse decomposition, the content generation result of the model is efficiently controlled by manipulating very few key components, and reliable and accurate control over the output content of the model is achieved under the condition that additional training is not needed.

This invention relates to the field of oil and gas geophysical exploration technology, and more specifically, to a time-domain compressed seismic spectral decomposition method based on matching pursuit. This method involves sparse decomposition of the seismic signal, specifically searching for the optimal matching wavelet, further deriving the reflection wavelet, and then performing Fourier transform and time-compression transform on the reflection wavelet to obtain its time-domain compressed spectrum. Finally, the time-domain compressed spectra of the reflection wavelet are superimposed and summed to improve the temporal resolution of the seismic record's time spectrum, avoid spurious spectral anomalies, and ensure the accuracy of the output results. Utilizing time-domain compression methods to improve the time-frequency resolution of data can highlight subtle features in the signal and their time-varying characteristics, improving the accuracy and reliability of signal processing. Applying this method to fields beyond seismic exploration can enhance signal feature recognition and improve signal detection capabilities.

The application relates to a source number estimation method based on orthogonal matching pursuit and signal subspace matching, and relates to a source number estimation method. The application aims to solve the problem that in an underwater acoustic environment, the signal-to-noise ratio of array snapshot data is usually not high, and is easily disturbed by colored noise and coherent sources, and for a high-speed moving target, the effective snapshot number is small, thus leading to low accuracy of traditional source number estimation. The application uses an orthogonal matching pursuit method to iteratively solve a support vector, and uses a characteristic vector corresponding to a maximum characteristic value of a residual to construct two subspaces, and realizes source number estimation through a signal subspace matching criterion. Simulation results show that compared with existing source number estimation methods, the application method has a lower requirement for a signal-to-noise ratio, performs better under small snapshot conditions, and is not sensitive to coherent sources and colored noise. Lake test data processing results show that the method can effectively estimate the number of fixed and moving underwater acoustic targets.

This application provides a method for diagnosing partial discharge (PD) of substation acoustic signals based on an improved matched pursuit network, relating to the field of substation acoustic signal PD diagnosis technology. The method includes segmenting and preprocessing the initial time-domain signal to obtain preprocessed time-domain segmented signals; performing time-frequency conversion on both noise-free and noisy analog signals to obtain noisy and noise-free time-frequency matrices; constructing multiple training sample pairs; inputting the noisy time-frequency matrix into an improved matched pursuit network to obtain an estimated feature matrix; calculating the loss value between the estimated feature matrix and the noise-free time-frequency matrix; optimizing the network parameters of the improved matched pursuit network to obtain an optimized improved matched pursuit network; inputting the real signal time-frequency matrix into the optimized improved matched pursuit network to obtain and identify a sparse time-frequency feature matrix, thus obtaining the PD type identification result of the current preprocessed time-domain segmented signal; improving the reliability and generalization ability of the diagnosis.

The invention discloses a seismic service system for micro-motion exploration and a data processing method, and relates to the technical field of geophysical exploration, and the system comprises the steps: deploying a distributed sensor array, and achieving the high-precision time synchronization through a self-organizing wireless network; continuously collecting environment vibration data and preprocessing the environment vibration data; calculating a cross-correlation function based on a phase weighted stacking algorithm to extract an empirical Green function; acquiring a high-precision Rayleigh wave phase velocity frequency dispersion curve by adopting a time-frequency domain phase matching pursuit technology; constructing a three-dimensional shear wave velocity model by combining a Markov chain Monte Carlo inversion algorithm of Bayesian reasoning, and introducing transverse constraint and prior geological information; and establishing a quality feedback control mechanism to optimize acquisition and processing parameters. According to the method, the stability of micro-motion signal processing and the resolution of underground structure imaging in a low signal-to-noise ratio environment are remarkably improved, full-process automation is realized by relying on a cloud service platform, and a single exploration processing period is shortened to be within 48 hours.

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

The invention relates to an aerial target recognition template library reconstruction method, device and equipment and a storage medium, and the method comprises the steps: building a scattering center evolution model which reflects a coupling relation between a target scattering center and an azimuth angle through the combination of attribute scattering center model prior and the deduction of a relation between scattering coefficient amplitude and the azimuth angle under a physical optical method; model parameter estimation is realized based on Newton orthogonal matching pursuit and a simplex method, and finally, a target identification template library is reconstructed based on SCEM. According to the method, accurate and steady recognition template library reconstruction can be realized under the condition of incomplete ISAR image samples, and the method does not depend on the number of samples.

This invention discloses an on-load tap changer vibration detection system and method based on wireless sensing and a photonic crystal resonator, relating to the field of smart grid equipment condition monitoring technology. The method includes: deploying wireless monitoring nodes to collect vibration signals; constructing an adjacency matrix based on mechanical topology connections; calculating the edge weights of the adjacency matrix using the physical distance and mechanical coupling strength between the wireless monitoring nodes; enhancing the vibration signals through optical-vibration coupling using a photonic crystal resonator to generate an amplified signal; compressing the amplified signal using a quantum compressed sensing algorithm to generate a compressed signal; performing orthogonal matching pursuit sparse reconstruction on the compressed signal to extract time-frequency joint features; and inputting the time-frequency joint features into a local convolutional neural network to generate local convolutional neural network parameters. This invention converts mechanical vibration signals into high-sensitivity modulated optical signals through the optical-vibration coupling effect of the photonic crystal resonator, solving the problem of low signal-to-noise ratio in strong noise environments using traditional sensors.

The application provides a matching pursuit strong coal seam reflection elimination method based on online dictionary learning, comprising the following steps: step 1, constructing a single-phase wavelet library based on online dictionary learning according to the seismic waveform characteristics of a target layer; step 2, performing matching pursuit signal decomposition on the target layer reflection signal based on the single-phase wavelet library of online dictionary learning; step 3, subtracting the extracted coal seam reflection from the original data to obtain a reflection profile after coal removal; and step 4, extracting a prestack gather coal seam reflection characteristic wavelet according to the coal seam reflection travel time, then eliminating or weakening the coal seam reflection, and performing prestack time migration on the coal-removed prestack data after deconvolution. The matching pursuit strong coal seam reflection elimination method based on online dictionary learning can effectively eliminate the influence of strong interference, thereby verifying the effectiveness of the technical scheme for eliminating strong coal seam reflection and improving the effective signal energy and resolution.

The application discloses a combined tracking passive radar fluctuation target detection method and belongs to the field of radar target detection. In view of the nonlinearity problem of the measurement equation under the passive radar system, the volume Kalman filter is adopted to update the posterior state of the fluctuation target in the current frame, and the posterior state of the current frame is used to predict the prior state of the next frame, and the prior state is used as the support set prior information for reconstructing the range-Doppler image of the next frame. A dynamic extension orthogonal matching pursuit method is adopted to determine the selection range of the target support set by using the prior state information of the target in the current frame. When the target tracking information accurately generates the target RD image, the CKF is adopted to track the motion of the fluctuation target under the passive radar system, and higher approximation accuracy is provided. The application is suitable for the field of radar target detection, can accurately reconstruct the RD image of the fluctuation target, and improves the continuous detection performance of the fluctuation target under the passive radar system.

The application discloses a CoSaMP pipeline defect positioning method based on NRBO adaptive sparsity, and belongs to the technical field of signal processing and nondestructive testing. The method comprises the following steps: performing pretreatment on the original echo signal collected, performing peak value detection on the echo signal, and adaptively completing window segmentation; a dictionary for sparse representation is constructed; a Newton-Raphson optimization algorithm NRBO is used to adaptively select a sparsity parameter k; based on the sparsity parameter k, a compressed sampling matching pursuit algorithm CoSaMP is used to complete sparse decomposition and reconstruction of the echo signal, and defect positioning is realized according to the reconstructed signal. The application realizes adaptive determination of the sparsity parameter through the NRBO, can reduce the subjectivity of parameter selection and improve the calculation efficiency under the premise of effectively preserving the key information of the echo signal, is suitable for defect echo signal processing and pipeline defect positioning scenes, and has good application prospect and popularization value.

The invention provides a metasurface off-grid DOA (direction of arrival) estimation method based on joint orthogonal matching pursuit. The metasurface off-grid DOA estimation method comprises the following steps: S1, constructing a joint over-complete dictionary containing a grid item matrix and a derivative item matrix; s2, setting initial parameters: a residual error and a support set; s3, calculating the inner product of the residual error and the joint over-complete dictionary to obtain a quantized value of the correlation between each column of the joint over-complete dictionary and the current residual error, and selecting a grid point index with the maximum joint correlation to be added into a support set; s4, utilizing a least square method to obtain a grid item and a derivative item coefficient corresponding to the current support set; s5, updating the residual error and circularly iterating S3 and S4 until a termination condition is met, namely, the number of iterations reaches the maximum value or the iteration error is smaller than a preset threshold value; and S6, according to the support set, the grid item and the derivative item coefficient, solving an off-grid offset and a spatial frequency estimation value, and realizing off-grid DOA estimation. The technical problem that an existing off-grid DOA estimation method is difficult to consider estimation precision, calculation complexity and algorithm robustness at the same time is solved.