142 results about "Phase correlation" patented technology

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention provides an AGV navigation texture feature extraction and identification method, which comprises the following steps: analyzing the spatial distribution position of a plurality of extracted matching peak values according to the matching peak values, counting the number of the peak values and the repeated spacing between the peak values, and identifying a positioning fuzzy region caused by texture cycle repetition; scanning a local aperiodic mark position according to the identified positioning fuzzy region, and performing uniqueness verification based on the geometric shape feature and color attribute of the mark to obtain a unique anchor point coordinate and a mark type; according to the unique anchor point coordinate, combining with the tile texture data to extract a texture repetition interval, analyzing the size of the texture repetition interval and a preset threshold value, and determining a periodic intensity value; dynamically adjusting a position screening range according to the periodic intensity value, establishing a screening constraint condition, and applying the screening constraint condition to a phase correlation method to obtain a preliminary positioning candidate point set;

The invention discloses a three-dimensional phase unwrapping method based on nonlinear phase coding, which comprises the following steps of: 1, constructing a wavelength-De Bruijn element mapping table, and generating a non-equal period phase shift stripe group; 2, projecting the phase shift fringes to a measured object frame by frame, obtaining a modulation pattern, and decoding the modulation pattern to obtain a non-linear wrapped phase; 3, constructing an approximate phase to carry out local phase unwrapping on the nonlinear wrapped phase to obtain a nonlinear local phase; and 4, screening out a phase candidate point and a candidate phase order of a nonlinear local phase by using a three-dimensional phase matching method. And step 5, using the phase candidate points to guide the nonlinear local phase to perform phase correlation matching so as to screen out a unique phase order, thereby realizing phase unwrapping. According to the method, a novel phase distortion characteristic is introduced, and a plurality of sine fringes with different wavelengths are used for serial coding according to a DB sequence. In a decoding stage, robust phase disambiguation is achieved by minimizing a local phase difference in left and right views. And high-precision measurement can be realized only by using three projection patterns, so that the three-dimensional measurement efficiency is greatly improved.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention discloses a remote sensing image inter-band registration method based on a phase correlation method. The remote sensing image inter-band registration method comprises the following steps: S1, partitioning a remote sensing image; s2, separating each wave band of each block of image, selecting one wave band as a reference wave band, and selecting other wave bands as to-be-registered wave bands; s3, converting each block image from a spatial domain to a frequency domain to obtain a frequency spectrogram of a wave band; s4, obtaining correlation and interaction information between wavebands in a frequency domain; s5, obtaining the correlation and the offset degree between the wave band images; s6, calculating the offset between the wavebands; s7, constructing a transformation matrix through the offset, transforming the to-be-registered waveband image, and synthesizing the to-be-registered waveband image in a reference waveband to obtain a registered block image; and S8, splicing the registered block images to obtain a registered integral remote sensing image. According to the method, the sub-pixel level registration between the remote sensing image bands is realized, and the algorithm operation efficiency is greatly improved.

The invention discloses a method and a device for constructing a simulation monitoring model of a transformer, a medium and equipment. The method comprises the following steps: firstly, acquiring an actually measured frequency response curve of the transformer under a specific frequency band signal; then constructing a simulation monitoring model, and randomly initializing the number of modules in the model; then performing simulation calculation to obtain a simulation frequency response curve; measuring the consistency between the actually measured data and the simulation data by calculating the amplitude-phase correlation degree; and if the amplitude-phase correlation degree and the current number of iterations do not meet the preset condition, iteratively adjusting model parameters through a multi-objective optimization algorithm until the amplitude-phase correlation degree and / or the current number of iterations meet the preset condition. By updating the simulation monitoring model in real time and dynamically comparing the simulation monitoring model with actually measured data, the accuracy and the real-time performance of transformer monitoring are improved. Compared with a traditional off-line fault diagnosis model, the method has the advantages that the difference between a simulation result and actual data is effectively reduced, and more accurate on-line monitoring is realized.

The invention provides an automatic door and window quality inspection method and system based on machine vision. A thermal image sequence and pressure data are synchronously collected in the pressurization process, a heat conduction base line is established in the low-pressure stage, and heat conduction mutation caused by deformation of the sealing structure is captured in the high-pressure stage. Dynamic registration is carried out on thermal image data based on door and window geometric features to generate a displacement vector field, and abnormal heat flow vortexes and discontinuous boundaries are identified through a spatial-temporal feature extraction model. And starting a dynamic data compensation mechanism to generate a tracking atlas in a pressure attenuation stage, finally analyzing the phase correlation of an abnormal region in combination with a three-dimensional seepage model, outputting an air leakage path parameter, comparing the air leakage path parameter with a sealing failure threshold value, and generating a partition sealing performance grading evaluation result. According to the technical scheme provided by the invention, through thermodynamic dynamic modeling and multi-source data fusion, precise positioning of non-contact door and window sealing defects, three-dimensional seepage path reconstruction and quantitative leakage evaluation are realized, and the quality inspection precision and efficiency are remarkably improved.

The invention relates to the technical field of image recognition, and discloses a grassland degradation recognition method and system based on aerial images, and the method comprises the following steps: S101, obtaining a three-band reflectivity image; s102, extracting a red edge main direction and red edge anisotropy intensity; s103, performing screening to obtain a main response band-pass texture map; step S104, calculating to obtain a red edge texture coherence ratio index; step S105, marking the types of the pixels; and step S106, obtaining the area and proportion of each type of region. Structural features such as red edge main direction and anisotropic strength of a red edge form comparison graph are extracted, adaptive texture features are screened in combination with multi-scale filtering and main response scale, the optimal displacement vector is calculated through phase correlation to achieve space alignment, pseudo-coherent interference such as splicing seams and micro-displacement is effectively inhibited in cooperation with weighted covariance, and the method is suitable for large-scale and large-scale imaging. Therefore, grassland degradation identification precision is improved.

The invention discloses a three-dimensional reconstruction method and system based on a two-dimensional foresight sonar image, and the method comprises the steps: 1, introducing a Mahalanobis distance detection method for a collected two-dimensional foresight sonar sequence, determining an initial contour of a local binary fitting level set segmentation method, and further, carrying out the segmentation of a local binary fitting level set; using a local binary fitting (LBF) level set segmentation method to determine a to-be-reconstructed region of the target on the basis of the detected initial contour of the target; and 2, registering the target area determined in the step 1 by using a registration method based on phase correlation, and calculating the relative displacement of sonar motion. And 3, based on a result of the above steps, reconstructing a three-dimensional point cloud image of the target by using a blind deconvolution algorithm. Compared with a traditional method, three-dimensional reconstruction can be performed on the target without acquiring horizontal position information of the sonar in advance, and the method has certain anti-noise capability. The method is more suitable for three-dimensional reconstruction of the underwater target.

The invention relates to the technical field of jitter analysis, in particular to a high-speed oscilloscope eye diagram real-time jitter detection method and system, and the method comprises the following steps: carrying out high-speed collection waveform zero-cross detection to obtain a timestamp, calculating offset grouping to obtain a cross-period edge time sequence, merging a jump cluster according to a sequence hierarchy cluster, calculating offset, obtaining a drift trend vector, and calculating the jitter of the drift trend vector; and based on the trend vector distribution compensation weight, weighting and correcting the time coordinate, calling a corrected coordinate detection period boundary to calculate a difference value exceeding a threshold value, triggering correction to obtain a period correction rear edge coordinate, and extracting a jitter frequency amplitude according to corrected coordinate Fourier analysis to generate an eye pattern jitter detection result. Phase correlation is established based on a cross-period sequence, an analyzable offset mode is formed through a clustering identification distribution structure, accumulated deviation is suppressed through a drift trend and weight correction, an abnormal period is calibrated through period dynamic discrimination, and the relation between a jitter structure and amplitude is separated through frequency analysis, so that the overall stability and integrity are enhanced.

The invention relates to the field of optical precision detection, in particular to a sub-aperture splicing detection method based on a frequency domain and space domain cooperative strategy, which comprises the following steps: collecting discrete phase data of sub-apertures; constructing a continuous physical field model; performing coarse alignment on the adjacent sub-apertures by using a frequency domain phase correlation technology to obtain integer pixel displacement between the adjacent sub-apertures; performing sub-pixel fine alignment through a numerical gradient method based on a normalized cross-correlation function of a continuous physical field model and an embedded geometric error elimination operator; constructing a global robust energy functional to carry out global optimization; and solving the zero-rotation pose under the floating reference, and performing Gaussian gradient weighted fusion and adaptive orthogonal substrate surface shape reconstruction to obtain the full-aperture surface shape of the large-aperture optical element. According to the method, the problems of high initial value dependence, displacement-inclination degeneracy, accumulative error warping, abnormal value sensitivity and the like of a traditional sub-aperture splicing detection method are solved, and ultrahigh precision and high robustness of large-aperture optical element sub-aperture splicing detection are realized.

The invention discloses a solid tire quality detection method based on infrared and deep learning, and relates to the technical field of tire quality detection, and the method comprises the following steps: in the high-speed operation process of a solid tire, collecting a current infrared image, and extracting a high-gradient thermal texture reference point based on a phase correlation operator; and based on the extracted hot texture reference point, mapping a reference point track in a previous frame of image to the current image through adaptive optical flow tracking, and generating a hot texture original displacement vector field. According to the method, real inter-frame displacement identification is realized through hot texture reference point extraction and optical flow tracking, high-frequency disturbance is suppressed in combination with frequency domain filtering, image position reference is unified through weighted fusion and standardized offset mapping, and meanwhile, low-quality frame input is effectively judged and controlled by introducing continuous verification and dynamic weight regulation. The consistency and robustness of image splicing are integrally improved, the defect recognition error is remarkably reduced, and the detection stability and accuracy of the deep learning model in the industrial environment are guaranteed.

A method is disclosed comprising: (a) receiving, at a receiver, a plurality of signals from a plurality of remote sources (S201); (b) providing a plurality of local signals using a frequency reference generated by a local oscillator; (c) determining a motion of the receiver (S203); (d) generating a search space parameterized by a hypothesised frequency offset, Δf, and a hypothesised frequency rate offset, Δf, of the frequency reference (S205); (e) populating the search space based on signal powers of a plurality of phase-compensated correlation signals corresponding to respective hypotheses of Δf and Δf (S209); and (f) selecting a preferred frequency offset as a frequency offset of the frequency reference based on the dataset (S213); wherein for each of the plurality of received signals used to populate the dataset, a plurality of phase-compensated correlations signals are calculated by: correlating the received signal with a corresponding one of the local signals to calculate a correlation signal; and calculating a phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal using a phasor sequence. A corresponding system is also disclosed.

The invention discloses a universal video image stabilization method based on multi-strategy fusion. The problem of video jitter in a complex dynamic scene is solved. Various algorithms are combined, so that the adaptability and robustness of the system are improved, and the method is widely applied to different types of camera equipment and video scenes. In a motion estimation link, an nR-AKAZE feature matching algorithm is adopted, feature points in video frames can be rapidly detected, and robust descriptors are generated. And then, Fourier transform is carried out through a phase correlation method, so that fine translation amount estimation is realized, and high-precision motion detection is ensured. An improved method based on parameter filtering is introduced in the motion compensation stage, and a Gaussian filter is used for smoothing a global motion vector, so that the influence of unintentional motion is reduced, and error accumulation is avoided. In addition, when the image-stabilized video frame is generated, the continuity and smooth transition of the visual effect are ensured through the image correction and zero value filling technology. And an efficient and accurate real-time video image stabilization processing solution is integrally provided.

The present invention provides a method for accurately rejecting defective materials in a rubber ring sorting device. The method comprises the following steps: Step S10, providing a circular turntable as a rotating platform. The turntable remains in a rotating state, and rubber rings sequentially fall onto the turntable via a preset slideway; Step S20, presetting an edge gap on the turntable, and using a slotted optical coupler installed thereon to determine the position as the zero point; Step S30, continuously capturing two images of the turntable with a camera, converting the two images into polar coordinates, and then performing a phase correlation operation; Step S40, determining the rotation angle value, and determining the turntable speed based on the time interval between camera captures; Step S50, determining the time to reject the material based on parameters such as the zero point, the turntable speed, and the time when the defective material was obtained; and Step S60, driving a lever device at the time of rejection to reject the material into a sorting bin. This method is intelligent, low-cost, and highly accurate.

The present invention relates to an online monitoring system and device for transformer core grounding current, comprising a signal acquisition unit, a conditioning unit, a data analysis unit, a storage unit, and a control unit. The control unit receives the measured value and historical data of the core grounding current, the measured value and historical data of the clamp grounding current, the measured value and historical data of the amplitude correlation coefficient, and the measured value and historical coefficient of the phase correlation coefficient, and comprehensively determines the fault state of the transformer core based on the received information. The present invention monitors the core grounding current and the clamp grounding current simultaneously, and after amplifying and filtering the corresponding grounding currents, analyzes the degree of correlation between the core grounding current and the clamp grounding current from different dimensions. By utilizing the principle that the correlation between the two grounding currents is significantly improved when the core and the clamp are turned on, the early insulation fault of the core and the clamp is assisted in the judgment, which can improve the detection rate of early insulation faults with the core grounding current lower than 100mA.

To achieve a small-scale circuit that obtains an output vector having a deflection angle based on the deflection angles of two input vectors as a new deflection angle.SOLUTION: A coordinate rotation processing device executes: processing of inputting a first input vector to vector input orthogonal coordinate data, and inputting a coefficient intensity vector having a coefficient intensity to rotation input orthogonal coordinate data, thereby generating, as rotation output orthogonal coordinate data, a coefficient intensity first vector having a deflection angle of the first input vector and having an intensity obtained by multiplying the coefficient intensity by a predetermined coefficient; and processing of inputting a second input vector to the vector input orthogonal coordinate data, and inputting a coefficient intensity first vector to the rotation input orthogonal coordinate data, thereby generating, as rotation output orthogonal coordinate data, a coefficient intensity second vector having the deflection angle obtained by adding or subtracting the deflection angle of the first input vector and the deflection angle of the second input vector and having an intensity obtained by multiplying the coefficient intensity by the square of a predetermined coefficient. The coefficient intensity is a predetermined value that is set for every set of vector pairs consisting of the first input vector and the second input vector.SELECTED DRAWING: Figure 4

The application discloses a partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals, comprising the following steps: S01, based on the RLS algorithm, the collected ultrasonic signals are subjected to noise reduction processing to eliminate white noise, and Fourier method is used to filter out interference signals below 20 kHz; S02, while the step 1 operation is being performed, the starting point of the collected ultrasonic signals is found out; S03, the original signal is down-sampled to 500 data points by using the discrete Fourier transform, the frequency spectrum is windowed and then inverse transformed, the fuzzy entropy of the down-sampled signal is calculated, and a first diagnosis model is constructed; S04, the sampling points of the original data are reduced to 3600 data points in the form of taking the maximum value at a certain interval. The partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals can effectively determine whether the switch cabinet has a partial discharge phenomenon, effectively reduces the false positive rate, and can be used for online detection and daily inspection of electrical equipment such as switch cabinets in multiple scenes.

The present application is applicable to the technical field of sub-pixel image registration, and provides a sub-pixel image registration method, apparatus and terminal equipment. The sub-pixel image registration method includes: obtaining a reference image of a target object and a target object to be registered; performing Fourier transform on the target object to be registered and the reference image; performing band-limiting processing on the target object to be registered after the Fourier transform, filtering out invalid information in the target object to be registered; and registering the target object to be registered after the band-limiting processing based on the reference image. The present application solves the problem that the existing phase correlation method fails at high magnification.

The application relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, wherein the infrared image enhancement method based on local phase correlation is used for acquiring continuous multiple infrared images, performing intelligent block division on a reference frame, calculating variance features, selecting an information-rich region of interest, independently performing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, improving robustness by using an outlier detection algorithm, and finally realizing sub-pixel level image alignment and intelligent weighted fusion. The application is suitable for real-time enhancement processing of spaceborne infrared remote sensing images, can guarantee processing quality while meeting the resource constraint requirements of an embedded platform, and provides an efficient and reliable technical scheme for space remote sensing image processing.

The present application belongs to the technical field of radar imaging, and relates to a kind of dual-base radar cooperative imaging method based on complementary random waveform, it is characterized in that, including the following steps: step 1: establish dual radar cooperative detection echo model, radar 1 and radar 2 all adopt random frequency hopping waveform;Step 2: carry out distance difference compensation, eliminate the phase modulation term of the phase difference of the echo signal received by radar 1 and radar 2, realize distance difference compensation;Step 3: carry out echo motion compensation, eliminate the component related to velocity component in echo, realize echo motion compensation;Step 4: carry out band fusion imaging, rearrange different frequency band random frequency hopping, carry out zero filling processing to echo data on the missing frequency point, and through setting tight constraint condition, make the echo signal of two radar through FFT transformation to realize phase correlation fusion imaging.The imaging method of the present application designs new frequency hopping waveform, realizes efficient motion compensation, and realizes band fusion by using phase correlation processing algorithm.

This invention relates to a forward-looking sonar SLAM method and system based on deep Fourier registration. The method includes the following steps: S1: acquiring two sonar images; S2: performing feature extraction, Fourier transform, and Radon transform on the two sonar images to calculate their angular correlation curves; S3: performing rotation correction on the two sonar images and performing feature extraction and phase correlation calculation to obtain a phase correlation heatmap; S4: calculating the rotation variance and displacement variance of the two sonar images; S5: obtaining the covariance matrix by weighted correction of the variance based on the SSIM correlation of the two images; S6: constructing a noise model and explicitly specifying the noise model parameters for each observation factor. This invention introduces Fourier registration technology and deep learning technology into the front end of the SLAM system, enabling the SLAM system to effectively eliminate interference caused by complex seabed environments and better adapt to complex underwater environments.

According to the microscope time sequence scanning image splicing method based on global position optimization, relative displacement is calculated through a phase correlation method, global position optimization is carried out in a spatial dimension, and a global position parameter with the minimum error is obtained. And then aligning the image at each moment to the global optimal position of the first frame by using a feature point-based time sequence alignment method to realize the minimization of time sequence jitter. The method can eliminate accumulative errors, improve splicing precision and reduce artifacts in dynamic analysis, and is suitable for the field of microscope image processing and splicing.

The invention provides a Sigmoid modulation phase correlation surface wave full waveform inversion method and system. The method comprises the following steps: acquiring a surface wave seismic record and constructing an initial S wave velocity model; performing forward modeling to generate a synthetic record; calculating instantaneous phase correlation of observation and synthetic records to construct an amplitude-independent target function; modulating the target function by using a Sigmoid function, and mapping the value of the target function to a predetermined interval; and calculating a gradient based on the modulated target function and iteratively updating the speed model until a termination condition is met, and outputting a final model. According to the method, the smooth saturation and bounded output characteristics of the Sigmoid function are utilized, the target function response is converted into a self-adaptive gradient scaling mechanism, a self-stabilization optimizer is introduced for the inversion process, and the convergence domain is widened by applying smooth constraint, so that the dependence on the initial model precision is reduced, and the first success rate and the result reliability of inversion under complex conditions are improved.

This invention relates to the field of prefabricated building installation accuracy control technology, specifically disclosing an intelligent calibration system for prefabricated building installation accuracy that integrates point cloud data and BIM. The system collects on-site point clouds and acquires design geometric information; after distance attenuation compensation for the point cloud reflection intensity, it generates on-site and design projection intensity maps along the normal direction of the component installation plane, and obtains the in-plane pre-translation amount through phase correlation calculation; using this pre-translation amount as the prior center, it constructs a spatial transformation objective function containing an exponentially growing dynamic penalty coefficient, and solves the final spatial transformation parameters through weighted centroid closed-loop inversion iteration; it calculates the signed vertical distance along the normal of the aligned point cloud and removes outliers to form a deviation field, outputting translation or rotation fine-tuning amounts according to the median deviation of the joint sub-region; this invention solves the in-plane sliding problem in the registration of flat components, improving the accuracy of installation deviation calculation and calibration reliability.

The embodiment of the invention discloses a picture movement detection method and device for image shooting and a storage medium, and the method comprises the steps: obtaining two successive frames of shot images; the method comprises the following steps: dividing a front frame of shot image and a rear frame of shot image into image blocks with the same number and size, and adding an anchor point in each image block; and counting the phase movement value of each image block in the two frames of shot images before and after through a phase correlation method, and judging whether the picture of the shot image moves or not. According to the invention, the two frames of shot images before and after are divided into the image blocks with the same number and size, so that the phase correlation method can be suitable for the local change of the image, and the overall movement (change) or local movement (change) detection of the shot image can be quickly and accurately carried out on the mobile intelligent terminal; anchor points are added in each image block, and aiming at the characteristics of a phase correlation method, on the premise that the speed and the accuracy are not influenced, the method has very high robustness for illumination variation.

The invention discloses an FPGA (Field Programmable Gate Array) hardware implementation architecture of a motion estimation algorithm, which is mainly applied to the field of video image stabilization, combines a sub-pixel precision estimation thought, and reduces the window size of Fourier transform by reasonably multiplexing a Fourier transform module, optimizing a hardware structure and performing down-sampling processing before phase correlation calculation. And after calculation, fitting the 3 * 3 neighborhood of the peak value to obtain a sub-pixel precision offset, and then amplifying the down-sampling multiple to obtain a final offset result. Compared with a basic implementation architecture, the improved architecture provided by the invention only uses two configurable Fourier transform modules, required hardware resources are greatly reduced, LUT is reduced by 45%, FF is reduced by 46%, and the detection range is expanded by 64 times.

The invention discloses a non-line-of-sight radiation source direct positioning method, which comprises the following steps of: constructing a signal receiving model for auxiliary positioning of an intelligent reflecting surface aiming at a scene of a plurality of to-be-observed targets, the intelligent reflecting surface and a receiving end; on the basis of the signal receiving model, discrete Fourier transform is utilized, and transform results of different frequency points are subjected to combined representation, so that a matrix related to the phase of the intelligent reflecting surface is determined; grid division is carried out on a plane space, and a sparse dictionary is constructed based on the matrix related to the phase of the intelligent reflecting surface; sparse recovery of the signals is carried out by using a sparse Bayesian learning method; and positioning the to-be-observed target based on the sparse dictionary by using a result after signal sparse recovery. The method provided by the invention has relatively high positioning precision and resolution in a scene with low signal-to-noise ratio and small snapshot.

The invention discloses a composite modulation signal blind identification method based on time-frequency analysis, which comprises the following steps of: firstly, judging and classifying input pulse signals, and then respectively carrying out identification and parameter estimation on determined signal types. According to the scheme, down-conversion analysis processing, signal time-frequency analysis processing and signal classification identification decision processing are firstly carried out on AD sampling intermediate frequency data sent by a digital signal processing board FPGA to obtain an identification result of a radar signal intra-pulse modulation type, and then corresponding parameter estimation is carried out according to the radar signal intra-pulse modulation type. According to two composite modulation styles of frequency coding and linear frequency modulation combination and frequency coding and two-phase coding combination, on the basis of time-frequency parameter characteristics of various composite modulations, related characteristic parameters are accurately extracted, a modulation type identification process is constructed, and time-frequency analysis is performed on radar signals through fast Fourier transform and phase correlation, so that the identification accuracy of the radar signals is improved. And furthermore, intra-pulse parameter estimation is accurately carried out on the radar signal.