1575 results about "Radar signals" patented technology

The invention discloses a radar signal modulation identification method and device for self-supervised contrast mask reconstruction, and belongs to the technical field of radar signal modulation identification, and the method comprises the following steps: obtaining a radar signal, constructing a radar modulation signal data set containing label data and label-free data, and converting the radar modulation signal data set into a time-frequency image; building a self-supervised contrast mask image reconstruction model comprising an online branch and a target branch; the method comprises the following steps: pre-training a self-supervised contrast mask image reconstruction model by using label-free data, performing data enhancement and random mask operation on a time-frequency image to generate double views, respectively inputting an online branch and a target branch, updating model parameters by jointly optimizing reconstruction loss and contrast loss, and obtaining a pre-training weight; and migrating the pre-training weight to a downstream identification network, freezing part of encoder parameters, and performing fine tuning by using a small amount of labeled data to obtain a radar signal modulation identification model. According to the invention, the radar signal modulation identification precision in a complex electromagnetic environment is improved.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

The invention relates to the technical field of radar electronic countermeasures, and particularly discloses a radar radiation source open set identification method (ARPLAD) based on countermeasure reciprocity point learning. The method comprises the steps that adaptive noise reduction and feature extraction are conducted on radar signals through a feature extraction network fusing a DRSN module and an ECA module, channel-level adaptive noise reduction is achieved through the DRSN by means of a soft threshold function, and key features are strengthened through dynamic weight distribution by means of the ECA; an adversarial reciprocity point learning framework is adopted, reciprocity points are set for each known class to serve as out-of-class space representation, and known and unknown class feature spaces are separated by maximizing the distance between known class samples and the corresponding reciprocity points; introducing a central loss function to compress intra-class feature distribution, and constructing a weighted total loss function optimization network in combination with an open space limitation function; and calculating a class self-adaptive threshold value based on the training sample, and comparing the maximum distance from the sample to each reciprocity point with the threshold value during testing to judge known and unknown classes.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

The invention discloses a hydropower station water level detection method and system. The method comprises the steps that an original water flow characteristic parameter set is obtained through multiple sets of Doppler effect radar flow measuring devices; performing segmentation extraction to obtain a water flow feature sub-data set; inputting a Transform network coding layer to generate a water flow feature coding vector; outputting a preliminary water level predicted value sequence through a decoding layer; a radar flow measurement model is called for correction to obtain a correction sequence; time sequence integration is carried out to obtain a water level change curve. The system comprises a multi-source Doppler radar signal acquisition unit, a spatial-temporal feature segmentation extraction unit, a Transform coding processing unit, a water level preliminary prediction unit, a radar model correction unit and a time sequence integration output unit. According to the method and system, multi-source parameters are deeply fused, the environmental adaptability is improved, the continuity and reliability of a detection result are enhanced, the water level of the hydropower station can be accurately monitored, and support is provided for scheduling decision and safety prevention and control.

The invention discloses a radar communication radiation source identification method and system based on multi-modal feature alignment, and belongs to the technical field of electronic reconnaissance and signal processing. The method comprises the following steps: preprocessing a received radar signal to generate a standardized time-frequency graph; extracting a signal feature vector through a specially designed convolutional neural network encoder; extracting a text feature vector by using a Transform encoder; through joint optimization of cosine similarity loss and physical parameter constraint loss, alignment of signal-text features in a unified vector space is realized; and finally, zero sample identification of unknown radar signals is realized through vector similarity calculation. According to the method, the problems of low recognition rate and insufficient cross-modal information fusion in a low signal-to-noise ratio environment of a traditional method are effectively solved, and the recognition precision and the system robustness are remarkably improved.

The invention discloses a radar signal noise reduction method for deep learning, and relates to the technical field of radar signal processing, and the method comprises the steps: converting a time domain radar signal into a two-dimensional time-frequency graph through short-time Fourier transform; frequency domain feature extraction is carried out on the two-dimensional time-frequency graph, and frequency domain feature representation is output through a DnCNN and a cascade CNN in sequence; performing time domain feature extraction on the time domain radar signal and outputting time domain feature representation; fusing the frequency domain features and the time domain features based on a multi-head attention mechanism to generate cross-domain joint features; and inputting the cross-domain joint feature into a residual shrinkage network for signal reconstruction, and outputting a denoised time domain signal. A time-frequency double-domain collaborative learning framework is constructed, and signal high-fidelity reconstruction in a complex noise environment is realized through deep fusion of time domain characteristics and a frequency domain structure distribution rule. According to the noise reduction method, an extremely low phase error and an extremely high feature retention rate can be kept in a strong noise environment.

The invention discloses a composite precipitation cloud layer identification method based on a laser radar-cloud radar, and the method comprises the steps: obtaining the observation data of the laser radar and the cloud radar, and carrying out the data matching of the observation data; processing a signal of the laser radar in a height interval of 18-20 km to obtain a preprocessed laser radar signal; performing laser radar cloud boundary identification based on a VED algorithm, and endowing different laser radar cloud scores according to the signal attenuation rate and the signal gradient; removing earth surface clutters by using a threshold method based on observation of spectral width and Doppler velocity; carrying out cloud radar significant echo recognition based on a threshold method, and determining the cloud score of the cloud radar according to different reflectivity factor signal intensities; determining a light rain area and a heavy rain area according to the Doppler velocity; cloud layer boundary information and a hairy rain area are obtained through cloud layer recognition result fusion, and cloud layer recognition and rainfall area recognition are completed; according to the invention, the accuracy of weather prediction is improved.

The invention relates to a calibration device (5) for calibrating radar-based level measuring devices (1) or their high-frequency module. For this purpose, the device (5) is based on a base body (51). A waveguide (52) runs within a planar plane (A) in this base body. The waveguide (52) is characterized by an input region (521), a coupling region for radar signals (S). HF , R HF ) reflective end region (522), and calibration section running between it (d kal ) out. According to the invention, the waveguide (52) or the calibration section (d) is kal ) are formed in a spirally nested configuration. This results in a spirally nested structure in relation to the length of the calibration section (d). kal ) or of the waveguide (52) achieves a very compact design, so that the calibration device (5) can also be used for recalibrations at the place of use of the level measuring device (1).

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention discloses a semi-supervised LPI radar signal modulation identification system and method based on entropy perception pseudo labels, and relates to the technical field of radar signal processing and mode identification. The system comprises a preprocessing module, a multi-scale reconstruction enhancer, a classification backbone network and a semi-supervised training module. The multi-scale reconstruction intensifier is used for reconstructing dual-channel separation through high-frequency detail enhancement and a low-frequency structure and enhancing discriminative characteristics in a noise environment; the classification backbone network introduces an adaptive contraction unit to realize channel-level noise suppression; and the semi-supervised training module dynamically evaluates the uncertainty of the unlabeled samples by adopting an entropy sensing mechanism, and generates weighted pseudo labels to carry out consistency regularization training. The method realizes signal modulation identification based on the system. According to the method, the problem of feature shielding under the condition of low signal-to-noise ratio is solved, the dependence of the model on labeled data is reduced through a reliable pseudo label generation mechanism, and stable and efficient modulation identification can still be realized in a severe channel environment with scarce labeled data.

The invention provides a radar signal depth feature extraction method and system based on adversarial sample defense, electronic equipment and a storage medium, and relates to the technical field of radar signal processing.The radar signal depth feature extraction method comprises the steps that a spacecraft synthetic aperture radar original echo signal is collected and converted into a time-frequency feature map, and intra-pulse and inter-pulse features are extracted in parallel to generate a combined matrix; receiving a deception jamming signal by using a polarization radar group, generating a signal data body without polarization influence through complex coherent superposition, inputting the deception jamming signal and the signal data body into a feature decoupling adversarial network, and separating bullet micro-motion target feature data by means of mutual information maximization constraint; and finally, inputting into a multi-scale local attention module to extract multi-band characteristic components, performing adaptive weight coefficient fusion according to power entropy, performing polarization channel energy correction, and outputting anti-interference fingerprint characteristics with micro-Doppler characteristic enhancement, so that anti-interference processing of the spacecraft synthetic aperture radar signals can be realized. And warhead target anti-interference fingerprint features with micro-Doppler feature enhancement characteristics are effectively extracted.

The invention provides an unmanned aerial vehicle countering method, which comprises the following steps of: acquiring a radar signal containing an unmanned aerial vehicle reflection signal in a target airspace through a radar detection module, obtaining an unmanned aerial vehicle motion feature through feature extraction, and inputting the unmanned aerial vehicle motion feature into a threat judgment model to determine a threat level. And if the active interception condition is met, controlling the radio interference module to transmit a directional interference beam and activate the physical interception module, and analyzing an interference efficiency parameter to generate a dynamic interception instruction. And finally, combining the target airspace coordinate, the threat level and the interception instruction into a countering control instruction, and sending the countering control instruction to a physical interception module to complete interception. According to the invention, automatic and accurate countering of the urban low-altitude unmanned aerial vehicle can be realized, and the response speed and interception reliability of a security and protection system are improved.

The invention belongs to the technical field of radar detection, and discloses a bird positioning method of a quantum radar bird detection system, which is characterized in that quantum characteristics such as polarization state and phase of echo photons are accurately analyzed by means of a quantum state demodulation technology, and high-precision measurement of the flight time of the photons is combined, so that the resolution limitation of the traditional radar is broken through from the quantum level; the technical combination not only can capture fine actions such as wing flapping frequency and steering angle of birds, but also can exert remarkable advantages in a dense bird flock scene: by utilizing quantum state differences of echo photons of different birds, overlapping signals are decomposed into independent individual signals through a quantum state separation algorithm; the individual distinguishing problem caused by traditional radar signal aliasing is solved; in high-precision demand scenes such as airports and the like, small bird targets can be accurately locked, flight potential safety hazards caused by misjudgment are greatly reduced, and the system can physically resist copying and deception interference of the outside to quantum signals based on the quantum unclonable principle.

A method for operating a radar system (210) in a vehicle (201), the method including establishing a wireless communication link (245) to a radio base station (250) in a wireless communication network (260), requesting a time-frequency resource (420, 430) for communicating with a network node (270) via the radio base station (250), receiving a transmission grant from the radio base station (250) for communicating with the network node (270) using the time-frequency resource (420, 430), transmitting a communications signal (245) in the time-frequency resource, and transmitting a radar signal (235) in the time-frequency resource.

The invention provides a radar signal multi-target tracking method and system based on a dynamic graph convolutional network. The method comprises the following steps: acquiring a radar one-dimensional range profile sequence of a target cluster, and performing pulse compression to form a radar echo two-dimensional sequence; separating position and amplitude data of aliasing scattering points based on scattering point distribution characteristics; calculating a spatial distance between targets by using position information, constructing a dynamic edge weight in combination with a radial speed difference, and generating a shielding relation edge; aggregating neighborhood features through a dynamic graph convolutional network, and learning trajectory decoupling features of the occlusion and intersection regions; and generating association weights based on the decoupling features, inputting the association weights into a multi-hypothesis tracking algorithm, generating a multi-branch trajectory, and if the spatial distance between two branches is smaller than a dynamic threshold value and the velocity vector included angle exceeds a set angle, retaining a branch with a higher association weight, and outputting a multi-target trajectory sequence. According to the invention, the multi-target tracking precision and robustness in a shielding and trajectory crossing scene are significantly improved.

The invention provides a radar signal classification method and system based on multi-modal feature adaptive fusion. The method comprises the following steps: performing compressed sensing processing on a time-frequency image by using a pre-constructed sparse sampling matrix to generate observation data; inputting the observation data into a multi-branch feature extraction network, and extracting local texture features and global semantic features through the multi-branch feature extraction network; calculating a global information theory feature tensor based on the time-frequency image, and generating a gating weight matrix based on the global information theory feature tensor; and performing adaptive fusion on the local texture features and the global semantic features by using the gating weight matrix to generate adaptive fusion features, and generating a classification result of the radar signals according to the adaptive fusion features. According to the technical scheme provided by the invention, the data dimension is reduced through compressed sensing, the complementary features are extracted by using the multi-branch network, and the precision and robustness of radar signal classification are effectively improved in combination with an adaptive fusion mechanism guided by an information theory.

The application discloses a kind of parameter agile radar signal intelligent sorting methods, first, original radar signal is detected, and pulse description word sequence is obtained, the pulse description word sequence obtained is preprocessed, then feature extraction module based on multi-branch hollow convolution, feature fusion module based on attention mechanism, radiation source mapping module based on transpose convolution are cascaded, and intelligent deep sorting network is constructed, then the sequence after pre-processing is used as the input of intelligent deep sorting network, and the intelligent deep sorting network is trained, finally, the parameter agile radar signal is sorted using the intelligent deep sorting network after training is completed.The method of the application can realize the accurate sorting of parameter agile radar, effectively solve the "batch" problem, has the advantages of flexible, accurate and strong generalization ability, can adapt to parameter overlap, noise and pulse loss and other complex situations.

A non-contact real-time monitoring system of physiological signs based on millimeter-wave radar includes a millimeter-wave radar and multiple modules for processing radar signals. The millimeter-wave radar is configured to continuously transmit electromagnetic wave signals and simultaneously receive echo signals, perform frequency mixing processing on the echo signals to obtain an intermediate frequency signal, and process the intermediate frequency signal to obtain a radar four-dimensional data matrix. Human body physiological signs are monitored by analyzing body thoracic cavity micro-motion information in signals through the modules; a target echo is processed by adopting a constant false alarm rate detection algorithm, and invalid signals are filtered. A self-adaptive range cell selection algorithm based on short-time stability of respiratory signals is adopted to capture radar echoes reflecting physiological movement. Mixed human body physiological sign signals are processed by using a VMD algorithm, and key parameters in VMD are optimized by using a GWO algorithm.

The invention relates to the technical field of target detection and identification, and discloses a low-altitude target identification system and method combined with multi-band signal processing. Comprising the steps of establishing a cross-frequency implicit vector model and constraining a geometric scale, a scattering center and rotor harmonic waves through synchronous acquisition of an L-band radar, a Ku-band radar and a millimeter-wave radar, and realizing unified characterization of multi-frequency signals; receiving a global satellite navigation system reflection signal, communication channel state information or a very high frequency / ultrahigh frequency passive radar signal to form prior discrimination, and triggering high-frequency radar short-time imaging; carrying out propagation correction on millimeter wave, terahertz and infrared signals in combination with a meteorological profile and generating an environment label; determining a sampling window by using the region of interest and rotor phase prediction, and optimizing a residence strategy; harmonic waves are extracted from radar and acoustic signals, robust pairing is carried out, and a cross-domain consistency score output category is calculated. The method realizes multi-frequency multi-source cooperative processing, has environment self-adaption and closed-loop feedback capabilities, and can improve the precision and stability of low-altitude target identification.

The invention discloses an unmanned aerial vehicle autonomous tracking method and system based on a millimeter wave radar, and belongs to the technical field of unmanned aerial vehicle autonomous tracking. According to the method, a radar-motion coupling model and an adaptive trajectory prediction algorithm are constructed, and after radar signal propagation distortion is dynamically compensated, a layered processing architecture is adopted to realize target separation and trajectory optimization; the front end solves a point cloud fuzzy problem through an adaptive Euclidean clustering algorithm, and the rear end completes trajectory prediction and control through an improved extended Kalman filtering and model prediction control composite architecture. The system mainly comprises a dynamic noise compensation module, a self-adaptive clustering module and a composite control module. According to the method, the problems of low tracking precision, large response delay and target adhesion of the unmanned aerial vehicle in a complex dynamic scene are solved, static target positioning errors and dynamic tracking errors are reduced, and high robustness and real-time performance are improved.

The invention relates to the technical field of data processing, and discloses a low-altitude radar information processing system based on big data analysis. The system comprises a data fusion module, a false alarm suppression module, a measurement target identification module and a grading early warning mechanism establishment module. Firstly, low-altitude radar signals are collected, space-time reference synchronization is carried out, original low-altitude radar data are obtained, and then data association matching is carried out by using a clustering fusion algorithm; secondly, optimizing the threshold value by using a multi-strategy fusion particle swarm algorithm, and performing false alarm suppression; establishing a three-path feature network model based on a multi-modal neural network architecture, carrying out target recognition, and outputting a low-altitude radar measurement target recognition result; and finally, according to a low-altitude radar measurement target identification result, carrying out environment constraint integration, generating a radar target motion track, and establishing a grading early warning mechanism. According to the method, low-altitude radar data are processed and modeled, the purpose of low-altitude radar information processing is achieved, and the method is accurate and objective.

The invention provides a low signal-to-noise ratio-oriented deep generative adversarial radar signal enhancement method and system. The method comprises the following steps of: extracting target scattering statistical characteristics from a preset historical database; generating an emission polarization state configuration instruction set according to the target scattering statistical characteristics; according to the emission polarization state configuration instruction set, obtaining an emission signal with a set polarization state parameter corresponding to the optimal emission polarization state, and emitting the emission signal in a low signal-to-noise ratio environment; receiving an echo signal generated after the signal is transmitted, and generating a noise polarization state parameter; obtaining a polarization filtering signal based on the noise polarization state parameter and a set polarization state parameter; based on target scattering statistical characteristics, obtaining a reconstructed polarization filtering signal, and obtaining an enhanced radar signal; according to the technical scheme provided by the invention, deep generative adversarial network reconstruction fusing optimal polarization emission, adaptive polarization filtering and target scattering characteristics is realized, and the radar echo signal quality and detection performance are improved in a low signal-to-noise ratio environment.

The invention belongs to the technical field of target detection, and particularly relates to a radar image restoration and small target detection method based on adaptive sparse modeling, and the method comprises the steps: collecting original radar signal data, obtaining an initial low-quality radar image through preprocessing, and constructing a signal feature template; carrying out adaptive step size division, sparse decomposition and dynamic threshold judgment to obtain a characteristic coefficient matrix; a double-norm dynamic weighting optimization model and a local noise sensing mechanism are utilized, and alternate iterative optimization is combined, so that a high-quality restored image is obtained; obtaining a signal point enhanced image through a three-scale Gaussian kernel collaborative detection and information entropy quantization weighted fusion strategy; outputting a signal point correlation structure chart and a structured feature matrix; and outputting a small target category probability through training and parallel reasoning by adopting a fusion network of an image path and a graph structure path. The method has the capabilities of small target high-precision detection, cross-equipment efficient adaptation and real-time processing in a complex scene.

The invention belongs to the technical field of human body posture reconstruction, and particularly provides an MIMO millimeter wave radar and infrared camera fused human body posture reconstruction method which is used for solving the problems that an existing human body posture reconstruction method is low in resolution, poor in stability, poor in reconstruction effect and the like. According to the method, a two-dimensional spatial spectrogram of a human body is extracted through a radar signal preprocessing algorithm, and two human body posture feature representations are obtained in combination with synchronously collected infrared images; then a fusion human body posture reconstruction model based on the MIMO millimeter wave radar and the infrared camera is constructed, effective extraction and fusion of two kinds of human body posture feature data are achieved through the fusion human body posture reconstruction model, high-precision and stable human body posture reconstruction is completed, and finally stable perception of the human body posture is achieved.

The invention discloses a nuclear magnetism safety interval confirmation device, and the device comprises a quantum noise suppression module which is used for eliminating radar signal noise caused by the quantum tunneling effect on the surface of a metal object in an ultrahigh field intensity environment; the multi-sensor fusion center is used for integrating the radar cleaning signal, the real-time magnetic field intensity data and the terahertz penetrability detection result to generate fused object position information; the dynamic magnetic field reconstruction engine is used for calculating a magnetic field distortion gradient by analyzing Hall sensor array data and a magnetohydrodynamic model in real time; the nonlinear danger scoring module is used for receiving object distance, magnetic field gradient, existence time and object classification data; the hierarchical response system is used for triggering a corresponding response according to the danger score; and the cesium atomic clock synchronization module is used for eliminating a timing error caused by a time crystal effect through a block chain timestamp verification mechanism. According to the invention, risk closed-loop control in a high-field-intensity MRI environment is truly realized.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention relates to a wind profile radar signal and data processing method and system, and the method comprises the steps: constructing a working parameter combination of a wind profile radar, and designing a radar detection mode of the wind profile radar based on the working parameter combination; the wind profile radar collects external wind field data based on the radar detection mode to obtain a wind profile radar signal; performing signal processing based on the obtained wind profile radar signal; performing pulse compression, moving target detection and Doppler spectrum analysis on the wind profile radar signal; extracting wind profile radar data from the processed wind profile radar signal, and processing the wind profile radar data; performing wind field inversion and quality control on the wind profile radar data; and constructing a three-dimensional wind field based on the processed wind profile radar data. According to the scheme, complete acquisition of weak meteorological echo signals under a clear sky condition is effectively ensured, and the accuracy of wind field inversion is improved.

The invention provides a radar signal anti-interference processing method and system based on a space-time attention mechanism. The method comprises the following steps: mapping an original horizontal polarization channel signal and an original vertical polarization channel signal into an interference suppression space, and generating a three-dimensional feature tensor comprising an amplitude component, a polarization phase difference component and an interference suppression residual component; according to the amplitude component, dividing a radar observation area into multiple layers of target blocks in a spatial dimension, and calculating a space-time association weight between adjacent layers of target blocks through a space-time attention mechanism; and according to the space-time correlation weight, screening out a plurality of target blocks meeting a preset continuous motion condition, and decoding a motion mode of the plurality of target blocks by using a long short-term memory network to generate an anti-interference target trajectory coordinate and a velocity vector. According to the method, the polarization characteristics and the space-time attention mechanism are fused, the anti-interference effect of radar signal processing in a strong interference environment is improved, and then the detection precision of the multi-target motion trail is improved.