5171 results about "Echo signal" patented technology

The invention relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention relates to a calibration method of an ultrasonic flaw detector calibrating device, which comprises the following steps of: acquiring and transmitting ultrasonic waves of which the frequency is matched with the theoretical frequency of a material to be detected, acquiring and dynamically correcting the actual propagation path of the ultrasonic waves in real time, generating a calibration parameter of a frequency-delay compensation coefficient, and compensating the transmitted ultrasonic waves based on the calibration parameter. After the compensated ultrasonic waves enter a to-be-detected material, time-base line signals are collected and input into the trained deviation calculation model to calculate time axis scale deviation, time-base line linearity is adjusted based on the time axis scale deviation, stepped ultrasonic attenuation signals are generated, echo signal amplitude values of the flaw detector are collected, and the amplitude values of the echo signals of the flaw detector are calculated. And comparing the echo signal amplitude value with a preset amplitude threshold value to obtain a dynamic difference value of the two, and adjusting the attenuation amount in real time based on the dynamic difference value. The problems of insufficient calibration efficiency and consistency, poor dynamic parameter adaptability, limited sensitivity calibration precision and complex working condition simulation capability in the prior art are solved.

The invention discloses a composite board ultrasonic defect detection method which comprises the following steps: S1, fixing a carbon fiber circumferential winding composite pressure container on a bracket, and establishing a coordinate system with the axis of the container as a z axis; s2, a 40 MHz dry coupling phased array ultrasonic probe is attached to the scanning starting point, and the contact angle of the probe is recorded; s3, moving the probe along the spiral track of the outer surface of the container at the speed of 20mm / s, and collecting A-scanning echo signals of all array elements; s4, performing pulse compression and time domain deconvolution processing on the acquired signal to obtain a time domain echo sequence; s5, delay time is calculated according to the shell curvature, the array element signals are compensated, and focusing B-scanning data are generated; s6, splicing the B-scanning data at the step length of 0.5 mm * 0.5 mm, and reconstructing C-scanning data corresponding to the space coordinates; and S7, inputting the C-scanning data into the trained Transform network, and outputting defect types, sizes and three-dimensional coordinates. The method realizes high-frequency ultrasonic composite board defect accurate detection, remarkably improves the microcrack recognition rate, and is widely applied to safety evaluation scenes of high-pressure hydrogen storage tanks and pressure vessels.

The invention relates to the technical field of new material detection, and particularly discloses an ultrasonic flaw detection method and system for a carbon steel bar. The method comprises the following steps: acquiring an appearance image and a section label, constructing a probe path, generating a sound field compensation factor, collecting and preprocessing an echo signal to form a multi-modal signal sequence, further extracting time sequence and spatial characteristics of the multi-modal signal sequence to generate fusion representation, and finally realizing defect classification and label generation. And dynamically updating model parameters and path configuration based on a classification result. The method can improve the detection intelligence and robustness under a complex structure, has high adaptability, high precision and high self-optimization capability, and is suitable for intelligent defect identification of industrial grade carbon steel materials.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

The invention discloses an intelligent material conveying deviation remote monitoring system based on Internet of Things, which relates to the technical field of material conveying deviation remote monitoring and comprises a signal monitoring module, an abnormal window calibration module, a distortion evaluation module, a data correction module and a remote optimization module. The distortion evaluation module is used for acquiring ultrasonic propagation distortion characteristic information in a signal propagation abnormal window in real time, analyzing and evaluating the echo signal distortion degree under the condition of ultrasonic signal propagation path distortion after acquisition, and dividing the echo signal distortion degree into three types, namely a light distortion degree, a medium distortion degree and a heavy distortion degree; and the data correction module is used for taking corresponding correction measures on the to-be-corrected measurement data under the condition of different echo signal distortion degrees based on the evaluation result. According to the method, the problem of measurement errors caused by propagation path distortion of ultrasonic signals in conveyor belt deviation monitoring is solved, accurate distortion evaluation and adaptive data correction are realized, and the monitoring precision and stability are improved.

The invention provides a lightweight AI personnel perception method and system based on a millimeter wave radar, and relates to the technical field of personnel perception, and the method comprises the steps: carrying out the phase compensation, amplitude calibration and bidirectional combined filtering processing of a millimeter wave echo signal, and constructing three-dimensional space point cloud data. Then, constructing a spatial distribution matrix based on the point cloud data, calculating the center position of a point cloud cluster, establishing an adaptive sampling radius to extract point cloud features, and calculating a motion vector and attitude parameters; and finally, inputting the motion vector and the attitude parameter into a double threshold judgment module, judging whether a person exists in the target area, generating a person position thermodynamic diagram based on the center position of the point cloud cluster, and outputting a person perception result. According to the method, the calculation complexity is reduced, the real-time performance and accuracy of personnel perception are improved, and the method is suitable for smart home, security monitoring and other scenes.

The invention discloses an electromagnetic ultrasonic thickness detection method and system based on a miniaturized low-power-consumption internet of things, and the method comprises the steps: carrying out the parameter optimization processing through a pulse coding excitation algorithm according to the electromagnetic characteristic parameters of a target material and a preset detection precision requirement, and outputting a low-power-consumption excitation parameter set; driving an electromagnetic ultrasonic transducer to generate a detection signal based on the low-power-consumption excitation parameter set, and outputting a compressed echo signal; performing multi-scale intrinsic mode decomposition on the compressed echo signal, and outputting a thickness correlation characteristic parameter set; inputting the thickness correlation characteristic parameter set into a depth residual error-attention network model, and outputting a thickness prediction result; and performing multi-node data fusion on the thickness prediction result based on an Internet of Things cloud platform to generate a final thickness detection report. According to the embodiment of the invention, high-precision and traceable thickness detection can be realized based on the miniaturized low-power-consumption Internet of Things.

The invention relates to a millimeter wave radar vital sign modeling method based on time-frequency characteristic decoupling. According to the method, a millimeter wave radar array is arranged, reflection echo signals are collected, and pure initial signal data are obtained; performing time-frequency transformation on the initial signal data based on a multi-scale sliding window to generate a time-frequency energy distribution map; according to the energy concentration degree and stability difference of different frequency components in the time-frequency spectrum, frequency components which are high in energy concentration degree and have continuous, stable and periodic changes on a time axis are extracted and serve as effective signal components corresponding to human respiration and heartbeat characteristics; extracting a corresponding frequency change trajectory according to the effective signal components, establishing a vital sign signal trajectory model based on a time-frequency characteristic change trend, and determining an optimal vital sign trajectory path according to the stability and continuity of the trajectory on a time axis; and establishing a vital sign monitoring model by using the optimal vital sign track path to realize high-precision monitoring of human respiration and heart rate.

The invention discloses a sonar target recognition system based on a residual network, and the system comprises a sonar preprocessing module which is used for collecting and preprocessing an original sonar echo signal; the frequency spectrum construction module is used for generating a multi-channel frequency spectrum image; the spectrum enhancement module is used for performing spectrum enhancement processing; the residual feature extraction module is used for extracting sonar target feature representation; the label screening module is used for calculating a label credibility score and constructing a label training set; the joint training module is used for combining the label training set and the real labeling samples to form a joint training set and executing iterative optimization of the SonarResBlock model; and the identification output module is used for performing reasoning identification by using the updated model and outputting a category label of the sonar target. According to the method, multi-class spectrum construction and a residual attention mechanism are fused, accurate sonar target recognition is achieved, and the method has the advantages of being comprehensive in modeling, high in robustness and efficient in training.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a rubber-lined pipeline defect detection method and system, and the method comprises the steps: arranging an ultrasonic probe array, obtaining large-area scanning information of a rubber-lined pipeline, collecting an ultrasonic signal, an echo signal and a resonance signal, generating a resonance signal curve, and reflecting a mapping image of the interior of the rubber-lined pipeline. And calculating a half-power bandwidth and a damping characteristic parameter, generating a corresponding characteristic vector through the identification model, identifying and positioning defects in the rubber-lined pipeline, evaluating the severity of the defects, and outputting an identification result. Through the structural design of arranging a plurality of array elements of the array probe, the coverage range is increased, ultrasonic wave emission and receiving are carried out on the rubber-lined pipeline from different angles and positions, the probability of discovering small-area debonding is improved, in addition, the machine learning technology is combined, an intelligent recognition model is trained, rich features are provided, and the detection accuracy is improved. The capability of identifying different types of defects is enhanced, the sensitivity to different types of defects is improved, and the risk of missing detection of small-area debonding is reduced.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

The invention discloses an atmosphere laser radar data analysis method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing atmosphere laser radar echo signal data, and generating a time series data set; s2, constructing a long sequence prediction model based on Transform, and forming a preliminary prediction result sequence; s3, optimizing model structure parameters and training parameters by using a particle filtering algorithm to obtain an optimized model; s4, using the optimized model to predict current and future moment data, and generating a final prediction sequence; s5, performing residual analysis on the final prediction sequence and the real-time observation data, and identifying abnormal points; and S6, sudden disturbance is detected, particle filtering optimization is restarted, and the prediction model is dynamically updated. According to the invention, by fusing the deep learning long sequence prediction model and the particle filter optimization algorithm, high-precision prediction and abnormal dynamic identification of atmospheric laser radar data are realized.

The invention provides a millimeter wave radar-based bridge deformation monitoring method and system, and the method comprises the steps: obtaining a transmitting signal and an echo signal of a radar, and carrying out the preprocessing of the signals, and obtaining an intermediate frequency signal; processing the intermediate frequency signal by adopting matched filtering and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by adopting a MUSIC algorithm to obtain target angle information; and obtaining a target distance angle unit by combining the target distance information and the target angle information, and obtaining target deformation information by adopting phase difference and phase unwrapping. According to the invention, the interference of noise and background can be avoided, the deformation displacement of the target area can be accurately calculated, and the real-time monitoring and analysis of the deformation state of the bridge can be realized.

The invention relates to a metal sheet detection method, system and device, computer equipment and a storage medium. The method comprises the following steps: controlling an ultrasonic guided-wave probe to generate an ultrasonic guided-wave signal under the condition that a metal sheet is transmitted to a preset detection area, so that the ultrasonic guided-wave signal is propagated in the metal sheet; acquiring a reflection echo signal of the metal sheet; performing feature analysis on the reflection echo signal to obtain a feature analysis result; and detecting whether the metal sheet has defects or not according to a feature analysis result to obtain a defect detection result of the metal sheet. The method is beneficial to improving the detection efficiency of the metal sheet.

The invention relates to the technical field of analyzing materials by utilizing an ultrasonic technology, in particular to a pipeline welding seam nondestructive testing method and system based on the ultrasonic technology, and the method comprises the following steps: acquiring echo signals collected by each ultrasonic probe at a welding seam position of a pipeline; analyzing the similarity degree of the time domain local features of the echo signals of each ultrasonic probe and the ultrasonic probe at the adjacent position under the same wavelet transform scale, and determining position similar probes and position similar parameters of each ultrasonic probe; according to the position similar parameters of the ultrasonic probe and the change difference of the echo signals of the ultrasonic probe and the corresponding position similar probe under each scale, determining a correction coefficient under each scale, and correcting a detail coefficient of the echo signals of the ultrasonic probe under each scale; obtaining a reconstruction signal based on the corrected detail coefficient under each scale; and comparing the reconstruction signal of each ultrasonic probe with the standard echo signal to detect the weld defect. By adopting the method, false detection and missing detection of tiny defects can be reduced.

The invention discloses a method for detecting probiotics in fermented rotten vegetable feed based on the Internet of Things, and relates to the technical field of microbiological detection, and the method comprises the following steps: obtaining complete echo signal time sequence data, and carrying out high-frequency sampling on an echo signal time sequence by a sliding time window with a fixed length, and constructing a three-dimensional optical signal transient feature vector. According to the method, the transient feature vector of the three-dimensional optical signal is constructed, and a disturbance residual error and time sensitive weight mechanism is introduced, so that the abnormal disturbance of the optical signal is accurately recognized; by combining the optical interference index and the dynamic sampling window adjustment strategy, the refractive index mutation area is automatically avoided, the sensing misjudgment and data distortion are avoided, the detection stability and accuracy of the probiotic activity state in the fermentation environment are remarkably improved, and the intelligent monitoring capability of the feed quality is enhanced.

The invention discloses a nondestructive testing device for a steel profile and a testing method of the nondestructive testing device. According to the method disclosed by the invention, the ultrasonic technology is combined with optical imaging, so that synchronous detection of surface and internal defects can be completed at one time. Ultrasonic waves penetrate through the interior of the steel profile, and hidden defects such as cracks and air holes are accurately recognized; the optical camera module captures visible problems such as surface scratches and corrosion in real time. The integrated detection mode avoids the tedious process of step-by-step operation required by a traditional method, for example, magnetic powder detection of surface defects and ultrasonic scanning of an internal structure are not needed, the detection period is greatly shortened, and the method is especially suitable for steel structure buildings, bridge engineering and other scenes with high requirements for detection efficiency and has good application prospects. The full-process automatic design effectively reduces the influence of human intervention, and ensures the scanning continuity of the detection area; the intelligent algorithm automatically analyzes the ultrasonic echo signal and the optical image data, and judges the defect level according to the preset standard, thereby avoiding subjective misjudgment of manual visual inspection.

The invention discloses an ultrasonic enhanced tactile sensor system, a sensor and a sensing method. The tactile sensor system comprises a multi-mode sensing unit and a processing unit. The multi-mode sensing unit integrates an ultrasonic module and a visual tactile sensing module, ultrasonic acoustic impedance matching and optical imaging transmission are achieved through an acousto-optic matching structure, and ultrasonic echo signals and tactile deformation images can be synchronously collected. In the processing unit, an event triggering module detects a contact event based on a tactile image and generates a switching signal, and a self-adaptive processing module dynamically controls an ultrasonic mode, distance measurement during non-contact, and switching to object internal imaging or material property identification during contact according to the switching signal. And the three-level fusion processing module outputs a multi-modal sensing result through signal-level synchronous acquisition, feature-level cross-modal fusion and decision-level task switching. According to the system, multi-dimensional perception of proximity sense, surface texture and deep information is realized, and the pre-judgment and accurate operation capability of the robot in a complex scene is improved.

The invention discloses a laser radar test method based on a long-distance target distance measurement result, and the method comprises the following steps: obtaining a laser radar echo signal, and extracting the frequency spectrum information of the echo signal; doppler frequency shift characteristic parameters are extracted according to the frequency spectrum information, and the relative speed of the target is calculated based on a preset frequency shift calculation model; acquiring multi-source sensor data associated with the laser radar test platform, and fusing to construct a dynamic ranging reference coordinate system; performing dynamic motion compensation on a laser ranging result according to the relative speed of the target to obtain a target initial measurement distance after Doppler correction; mapping the target initial measurement distance after Doppler correction to the dynamic distance measurement reference coordinate system to obtain a remote target final distance measurement result aligned with a test platform coordinate system; and outputting the accurate position and moving state information of the target in the three-dimensional space according to the final distance measurement result. According to the invention, the ranging precision and stability can be improved.

The invention provides an underwater ultrasonic Doppler real-time analysis method and device based on a digital transducer, and relates to the technical field of data analys.The method comprises the steps that 1, multiple beams of directional ultrasonic waves are emitted into drilling fluid through a digital transducer array on the outer wall of a marine riser, echo signals reflected by gas-liquid two-phase flow are received, and the digital transducer array is used for receiving the echo signals; performing real-time digital conversion on the echo signal to generate an original digital signal containing phase and amplitude information; 2, Doppler frequency shift feature extraction is conducted on the original digital signals, and the real-time movement speed and direction of bubbles in the drilling fluid are determined by calculating the frequency deviation of reflected ultrasonic waves and transmitted ultrasonic waves. Through the digital transducer array and the ultrasonic Doppler technology, accurate measurement of motion parameters of bubbles in drilling fluid, real-time calculation of local gas content and early warning and grading response of gas cut are achieved, and the safety and the intelligent level of drilling operation are improved.

The invention discloses a laser radar multi-gas identification method and system based on deep learning, and relates to the technical field of laser radars, and the method comprises the steps: employing a differential absorption laser radar to emit lasers with different wavelengths, receiving echo signals after gas absorption, extracting multi-domain features, and employing multi-target optimization in combination with feature importance evaluation to screen key features; generating a gas portrait of the potential gas according to the key features, and obtaining the type of the interested gas; constructing a graph structure according to the gas portrait and the gas feature similarity, performing representation learning on the graph structure by using a graph convolutional network, enhancing the graph convolutional network by using an interested gas category, constructing a gas category branch, and outputting a gas category; gas concentration branches are built by adopting a multi-layer sensor, final node representation output by the graph convolutional network is used as input, and each gas concentration is output by using linear regression. According to the invention, the distinguishing capability of small difference gases is improved, and high-precision identification and concentration inversion of multi-component gases are realized.

The invention discloses a marine surveying and mapping method based on multi-beam sounding correction, and belongs to the technical field of marine surveying and mapping. The method comprises the following steps: acquiring sounding echo signals, sound wave propagation time delay data and temperature data through a multi-beam sonar array, an acoustic chromatography transceiving array and a laser Raman probe; and fusing the data to construct a dynamic refractive index field model, correcting sound ray refraction distortion by adopting an adaptive variable step size algorithm, carrying out weight allocation and data fusion on space-time adjacent beams to obtain high-confidence water depth, and generating an isobath topographic map. The problem of refraction distortion is solved through multi-source data fusion, the sound ray tracking step length is dynamically optimized, noise interference is suppressed through weighted fusion, and the precision and reliability of topographic surveying and mapping in complex environments such as a deep sea hydrothermal region are remarkably improved.

The invention relates to the technical field of echo detection, in particular to an active sonar echo detection method, which comprises the steps of collecting active sonar reverberation data; constructing an active sonar channel, and generating reverberation data of a target echo; extracting target echo signal features by using matched filtering; a classification model of an improved SwinTransform is constructed, pooling, full connection and residual structures are constructed behind a Patch Merge layer of a hierarchical model of different stages, a transient target peak value in a reverberation background is highlighted, and time-frequency correlation characteristics of reverberation and a target are captured. According to the method, the problem that the accuracy of predicting whether a target exists or not by using sonar echoes through a Swin Transformer model needs to be further improved is solved.

The invention relates to the technical field of automatic driving, and discloses a parking assisting method and system for automatic driving. The method comprises the following steps: cascading and splicing a first fisheye image acquired by a main camera in a vehicle body and a second fisheye image acquired by a slave camera to obtain a panoramic image; performing parking space boundary identification and obstacle analysis on the panoramic image to obtain parking space boundary information and obstacle position information, and synchronously performing distance calculation on a radar echo signal of an ultrasonic sensor to obtain distance measurement data; and a parking environment map is created according to the parking space boundary information, the obstacle position information and the distance measurement data, path planning is carried out based on the parking environment map, a parking execution instruction is obtained, and the parking execution instruction is used for vehicle parking auxiliary control. According to the method, the view coverage range and the flexibility of the observation angle are expanded, and the execution precision and safety of parking auxiliary control are improved.

The invention provides a falling detection method and system based on millimeter wave radar fusion human body postures, and relates to the technical field of falling detection, and the method comprises the steps: collecting human body echo signals, and constructing three-dimensional point cloud data through distance, angle and speed estimation; the point cloud is processed to generate a dynamic sequence, and skeleton features and key point coordinates are extracted in combination with a graph convolutional network. A skeleton connection relation is constructed based on the key points, attitude features are calculated, key change features are extracted through a self-attention mechanism, and the key change features are fused with a point cloud sequence to construct multi-modal features. And the double-branch state recognition network processes the fusion features, and when abnormality is detected, further analysis is carried out through residual attention and space-time diagram convolution, and finally, the falling state is recognized and the risk level is evaluated.

The invention relates to a single-channel moving target refocusing method and device based on a bunching mode, and the method comprises the steps: detecting a ground moving scene through a terahertz video radar, obtaining a single-channel echo signal, generating a two-dimensional image of the ground moving scene, and extracting a target image of a moving target from the two-dimensional image; the method comprises the following steps of: performing azimuth inverse Fourier transform on a target image to obtain a one-dimensional range image, performing secondary phase compensation by using an image displacement algorithm, and performing range envelope alignment on a compensated signal by using an adjacent cross-correlation algorithm of linear interpolation to correct a nonlinear error term so as to obtain a moving target refocusing image after the nonlinear error term is corrected. And the two-dimensional image is filled with the image to obtain a complete image. By adopting the method, the problem of image defocusing caused by micro-distance migration can be effectively solved.

The invention discloses an unmanned aerial vehicle target track auxiliary association method, equipment and a medium, belongs to the technical field of radar target tracking, and is used for solving the problems that an existing track association method mainly depends on a fixed rule, when a plurality of trace points exist, wrong trace points are often associated due to the limitation of the rule, and the correlation efficiency is poor. And a relatively high error association risk exists. The method comprises the following steps: according to an echo signal in a target airspace, performing plot score calculation of related main peak plot micro-motion characteristics on Doppler dimension slices of each plot to obtain a main peak plot score; based on the unmanned aerial vehicle trace point membership degree corresponding to the main peak trace point score, non-coherent accumulation is carried out on the Doppler dimension signal of the target track, and a target marking unmanned aerial vehicle is discriminated and determined; and carrying out track correlation matching processing on the target marking unmanned aerial vehicle to obtain a latest Doppler signal under the correlation trace point.

The invention discloses a real-time dynamic trajectory tracking method and system for millimeter wave radar gesture recognition, and relates to the technical field of gesture recognition tracking, and the method comprises the steps: receiving an echo signal reflected by a gesture, extracting a potential target point cloud, and carrying out the clustering generation of a gesture point cloud sequence; establishing a multi-modal motion model library, dynamically selecting an optimal motion model by adopting graph matching, and generating a prediction state in combination with a gesture point cloud sequence; on the basis of a Poisson multi-Bernoulli hybrid filtering framework, according to the signal-to-noise ratio and spatial distribution of the current gesture point cloud sequence, dynamically adjusting the observation weight, optimizing the observation point cloud, and carrying out optimal association by combining Mahalanobis distance with dynamic time warping; a multi-hypothesis tracking strategy is adopted to maintain trajectory hypothesis, an optimal trajectory is selected through a trajectory scoring mechanism, and Kalman filtering smoothing processing is performed on the optimal trajectory. According to the method, high-precision and low-delay tracking of gesture motion is realized, gesture habits of different users and complex environment interference can be adapted, and meanwhile, relatively high track precision is kept.