178 results about "Micro doppler" patented technology

The invention relates to the technical field of air target prediction, in particular to an air target trend prediction method, which comprises the following steps: S1, multi-source heterogeneous data adaptive fusion filtering processing; s2, manifold learning is constructed in the high-dimensional spatial-temporal feature space; s3, performing semantic modeling on the dynamic behavior pattern recognition intention; and S4, multi-dimensional threat situation assessment warfare area modeling is carried out. According to the method, high-precision space-time synchronization and noise suppression of radar sensor data, infrared sensor data and other sensor data are achieved through the multi-source heterogeneous data self-adaptive fusion filtering technology, target micro-Doppler features are effectively reserved, missing data are repaired, and through the combination of third-order Savitzky-Golay differential filtering and short-time Fourier transform, high-precision space-time synchronization and noise suppression of radar sensor data and infrared sensor data are achieved. An 18-dimensional compression feature space containing kinematics and electromagnetic characteristics is constructed, a nonlinear topological relation is reserved through t-SNE and an automatic encoder, the signal-to-noise ratio and feature expression capacity of original data are remarkably improved through the function, and a high-precision and low-redundancy input basis is provided for follow-up behavior recognition and prediction.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

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 relates to the field of low-altitude monitoring and target identification, and discloses a dynamic target detection method based on a three-dimensional low-altitude monitoring radar. The method comprises the following steps: performing three-dimensional scanning to obtain low-altitude echoes, and resolving three-dimensional coordinates of a target based on Doppler frequency shift and array angle measurement; clutters are suppressed by adopting space-time adaptive processing and constant false alarm rate detection; separating real echo and mirror image interference by using a sparse reconstruction method; fine classification of dynamic targets such as an unmanned aerial vehicle is realized through micro-Doppler features; judging abnormity in combination with trajectory entropy and divergence, and correcting refraction errors by using atmospheric stratification imaging; and further identifying a group cooperation behavior based on graph clustering and a speed consistency index, and outputting multi-level layered early warning information. Compared with the prior art, a closed-loop link from single-target detection to group situation analysis is realized, high reliability, anti-interference performance and pre-warning capability are realized, and the detection precision and situation awareness level in a complex low-altitude environment can be remarkably improved.

The invention provides a crane commander gesture recognition method based on a multi-branch G-LSA millimeter wave radar, and the method comprises the steps: transmitting a frequency modulation signal through an FMCW millimeter wave radar, and receiving an echo signal reflected by a gesture; performing frequency mixing and processing on the emission signal and the echo signal to obtain a beat frequency signal, and acquiring gesture signal data of a commander in a real application environment through ADC sampling; performing signal preprocessing on the sample to obtain micro-Doppler features of the sample, establishing a corresponding point cloud data set D, and performing unified processing on the micro-Doppler features of the sample to obtain a three-dimensional mixed feature tensor; constructing a gesture recognition model based on multi-branch G-LSA, training the model, and optimizing the weight of the model to obtain a gesture recognition model; aDC data of gesture actions of a crane commander are collected, and a three-dimensional mixed feature tensor of the ADC data is obtained and input into a multi-branch G-LSA gesture recognition model; an identification result is sent to a crane cab; according to the method, a safer and more reliable gesture interaction scheme is provided for high-precision and high-risk scene optimization such as crane command.

The invention discloses a millimeter wave radar signal gesture micro-Doppler feature extraction method based on a GPU, and belongs to the technical field of radar signal processing. The invention aims to solve the problem that the processing speed and the extraction precision are difficult to consider at the same time due to large operand and poor real-time performance when the gesture micro-Doppler signal is processed by a traditional CPU (Central Processing Unit). According to the technical scheme, the method comprises the following steps: performing phase intervention and high-resolution two-dimensional Fourier transform on multi-channel original echoes through a GPU (Graphics Processing Unit) to generate a distance-Doppler spectrum of each channel; performing amplitude accumulation fusion on the distance-Doppler spectrum of each channel to enhance a signal, and discriminating a hand target area by adopting a two-dimensional self-adaptive operator; and finally, integrating continuous multi-frame detection results, constructing a micro-Doppler characteristic spectrogram representing fine gesture motion through time-frequency analysis, and extracting quantized micro-motion characteristics from the micro-Doppler characteristic spectrogram. The method can be used for accurately extracting the micro-Doppler features of the gestures at a high speed, and is mainly applied to the fields of real-time human-computer interaction, intelligent control and the like.

The invention discloses an unmanned aerial vehicle target detection and micro-Doppler parameter estimation method and device, and the method comprises the steps: carrying out the target detection of an unmanned aerial vehicle through detecting an echo signal by employing a GLRT detector; when the unmanned aerial vehicle target is detected, estimating the Doppler frequency shift of the unmanned aerial vehicle target by using a first estimator; the first estimator is designed based on a DE algorithm; the Doppler frequency shift is determined based on the radar position of the unmanned aerial vehicle main body part, the unmanned aerial vehicle position and the unmanned aerial vehicle speed; estimating the micro-Doppler frequency shift of the unmanned aerial vehicle target by using a second estimator; the second estimator is designed based on a PSO algorithm; the micro-Doppler frequency shift is determined based on the rotating speed and the initial phase of the rotor wing part of the unmanned aerial vehicle, so that the efficient and high-precision unmanned aerial vehicle target detection and micro-Doppler parameter estimation method is realized.

The invention belongs to the technical field of key point detection, and particularly relates to a millimeter wave radar gesture key point detection method, system and device based on double-flow depth fusion and a medium, and the method comprises the steps: carrying out the preprocessing of hand motion data, obtaining a distance-Doppler three-dimensional space-time tensor, a micro-Doppler spectrogram and a key point truth value sequence, distance-Doppler three-dimensional space-time tensor and micro-Doppler spectrogram features are extracted, cross-modal feature fusion is carried out, a key point detection decoder is designed, a gesture key point prediction coordinate sequence is estimated based on modal fusion features, and a distance-Doppler three-dimensional space-time tensor and micro-Doppler spectrogram features are obtained; screening the first m gesture key point prediction coordinate sequences with the highest confidence score, performing optimal matching with the key point true value sequence through a Hungary algorithm to obtain overall regression loss, and performing iterative training to complete a gesture key point detection model; systems, devices, and media for implementing the methods thereof; the method has the comprehensive application advantages of high precision, high robustness and low cost.

The invention discloses a multi-mode human body recognition system and method based on photoelectric metasurface and radar fusion, and belongs to the technical field of optoelectronics and radar perception. According to the system, optical edge enhancement is realized through a phase programmable metasurface, a continuous wave radar and a frequency modulation continuous wave laser radar are combined to obtain a micro-Doppler spectrum and a three-dimensional point cloud, and a near-infrared reflection spectrum is collected at the same time. According to the algorithm, a cross-modal Transform-GAT architecture is adopted to fuse multi-source features, edge extraction is optimized through a self-adaptive kernel function, and low-power-consumption edge reasoning is achieved through a Mach-Zehnder interference device or a ReRAM chip. Experiments show that the recognition accuracy of the method in complex scenes such as multi-target shielding and low light is remarkably superior to that of a single-mode scheme, the method has the edge deployment capability with delay smaller than or equal to 25 ms and power consumption smaller than or equal to 4 W, and an efficient solution is provided for multi-scene human body dynamic recognition and health monitoring.

The invention discloses an unmanned aerial vehicle-to-vehicle multipath channel modeling method considering unmanned aerial vehicle body shielding and six-dimensional motion characteristics, and the method comprises the steps: carrying out the modeling of a channel between a transmitting antenna and a receiving antenna, and obtaining a channel model; calculating position vectors and velocity vectors of the unmanned aerial vehicle, the vehicle, the transmitting-receiving end antenna and the scatterer in the channel model; calculating macro Doppler frequency shift of each signal transmission path in a three-dimensional translation scene of the unmanned aerial vehicle in the channel model; calculating the micro-Doppler frequency shift of each signal transmission path in the unmanned aerial vehicle three-dimensional rotation scene in the channel model; calculating a space-time correlation function of a transmission signal in a six-dimensional motion scene of the unmanned aerial vehicle in the channel model; and calculating the time-varying Doppler power spectrum density of the transmission signal in the six-dimensional motion scene of the unmanned aerial vehicle in the channel model. According to the method, the influence of the six-dimensional motion of the unmanned aerial vehicle on the multipath channel Doppler effect and the channel statistical characteristics is comprehensively analyzed, and comprehensive analysis of the unmanned aerial vehicle on the multipath channel characteristics of the vehicle can be provided.

The invention discloses an unmanned aerial vehicle multi-mode fusion radio positioning and signal identification method and system, and relates to the technical field of radio positioning and detection.The method comprises the steps that an airspace radio environment is continuously monitored in a monitoring area through all receiving stations, a power spectrum sequence and an unmanned aerial vehicle communication signal are obtained, and spectrum features are extracted from the power spectrum sequence; for the same launch event, acquiring a three-dimensional motion track of the unmanned aerial vehicle according to the arrival time of each receiving station; echo signals generated by unmanned aerial vehicle rotor wing and vehicle body movement are obtained through a radar, and micro-Doppler features are extracted from the echo signals; acquiring protocol related modulation features, cyclic spectrum features and protocol features according to the unmanned aerial vehicle communication signals; performing multi-modal feature fusion on the spectrum feature, the micro-Doppler feature, the modulation feature, the cyclic spectrum feature and the protocol feature to obtain a deep joint feature; identifying the type of the unmanned aerial vehicle according to the deep joint feature, and obtaining a type label; and constructing an unmanned aerial vehicle feature signature by using the category label, the three-dimensional motion trail and the depth joint feature. According to the invention, the accuracy, robustness and supervision capability of unmanned aerial vehicle positioning and signal detection in a complex environment can be improved.

The invention relates to the technical field of unmanned aerial vehicle detection and security and protection, and discloses a low-altitude unmanned aerial vehicle dual-mode detection method based on micro Doppler radar and infrared thermal imaging. According to the method, a monitoring area is continuously scanned through a micro-Doppler radar, and when a suspected target with the signal intensity lower than a preset threshold value is recognized, an infrared thermal imaging device is triggered to be started. The infrared device directly adjusts the orientation and focuses to a specific area according to the target orientation and distance information provided by the radar, and obtains a high-resolution thermal imaging sequence. And then the radiation temperature distribution, the temperature gradient and the thermal profile morphological characteristics of the target are analyzed, and final judgment is completed. According to the invention, on-demand starting and rapid and accurate guiding of the infrared sensor are realized, the overall power consumption of the system is reduced, and the cooperative detection response speed and the identification precision of the rapid moving target are improved at the same time.

The invention discloses a low-altitude unmanned aerial vehicle countering monitoring system based on state feature decoding, relates to the technical field of unmanned aerial vehicle prevention and control, and is used for solving the problem of poor low-altitude target detection and countering. According to the invention, through closed-loop management and control of heterogeneous radar networking detection, signal level fusion processing, threat assessment and resource scheduling and intelligent countering decision, accurate identification and directional countering of the low-altitude unmanned aerial vehicle are realized; according to the method, the detection precision and the micro-Doppler feature extraction capability of a low-speed small target are improved by combining a virtual aperture synthesis technology, then unbalanced resource allocation is implemented based on a threat level, the system efficiency is optimized, and finally seamless migration of an interfered node task is realized by constructing a signal interference situation map. And continuous tracking and effect evaluation on the target are maintained in the active radio frequency countering process, so that the overall efficiency and reliability of low-altitude security and protection in a complex environment are effectively improved.

The invention provides an engineering robot positioning method and system fusing a visual system and a laser system, and the method comprises the following steps: collecting a preset reflection array label image through the visual system, and rapidly solving the initial six-degree-of-freedom posture of an engineering robot; based on the initial attitude, a laser system is guided to actively detect beacons on a label, attitude angle drift is accurately corrected by calculating spatial difference vectors between the beacons, and the micro-Doppler effect analysis of reflected laser is utilized to determine the micro-vibration state of the tail end of the robot in real time. And calculating respective data confidence of vision and laser according to image quality and laser signal stability, fusing the initial attitude and the drift correction through a nonlinear optimization algorithm, and outputting a final high-precision six-degree-of-freedom attitude. According to the invention, high-precision robot positioning can be realized by fusing global perception of vision and local accurate measurement of laser.

The invention relates to a target behavior identification method and device based on space-time frequency and image domain micro-Doppler characteristics, and the method comprises the steps: generating a time domain signal of a target region based on a radar echo signal of the target region; a two-dimensional image of the target area is generated according to the time domain signal, and the two-dimensional space position of at least one object is determined; according to the two-dimensional space position, multi-height-point single-point three-dimensional imaging is carried out on at least one object at different height points, and a single-point three-dimensional imaging result is obtained; and finally, according to the single-point three-dimensional imaging result of the multiple height points in the continuous time, generating a micro-Doppler spectrogram of at least one object, and according to the micro-Doppler spectrogram, identifying the behavior of the at least one object. According to the method, the space, time and frequency characteristics are combined through the space-time-frequency cooperation framework, the object behavior recognition precision is improved, and efficient multi-object behavior recognition can be achieved in a complex scene.

The invention discloses a robust human body existence sensing method and system based on a millimeter wave radar in a complex interference environment, and aims to realize high-precision and high-robustness sensing of a personnel existence state in a complex indoor scene through a signal processing and multi-index fusion mechanism. The system takes a millimeter-wave radar as a core sensor, and through a signal processing flow comprising adaptive background modeling and coherent subtraction based on a complex number distance spectrum, a cleanliness index constructed by extracting multi-dimensional features from a micro-Doppler spectrum, and a dual-channel parallel detection mechanism for moving and static targets, the detection precision of the moving and static targets is improved. The core challenges that static detection is difficult, environment interference is sensitive, and system adaptability is insufficient are effectively overcome. According to the method, the environment confidence, the signal cleanliness, the moving target evidence and the static target evidence are fused for layered joint judgment, non-target movement and real human body movement can be accurately distinguished in the indoor environment with various typical interferences, and continuous and reliable detection of the full state of the person from moving to static is achieved.

A target recognition method, a storage medium and an electronic apparatus, the method comprising: acquiring the micro-Doppler frequency of a target on the basis of the Doppler frequency of the target, so as to construct a time-delay micro-Doppler trajectory map, the Doppler frequency and the micro-Doppler frequency having time delay characteristics; acquiring micro-Doppler energy spectrum features of the target on the basis of the time-delay micro-Doppler trajectory map; and recognizing the target on the basis of the micro-Doppler energy spectrum features.

The invention discloses a millimeter wave radar human body existence sensing method capable of eliminating interference in blocks, and belongs to the technical field of human body existence sensing, and the method comprises the following steps: S1, a millimeter wave radar emits an electromagnetic wave signal indoors, and receives an echo signal when encountering a static object and a vibrating object; s2, Doppler frequency shift phenomenon information of a plurality of vibration objects in the echo signals is extracted, and feature vectors are formed; and S3, clustering the feature vectors, and dividing the spatial region into a plurality of blocks by using K-means and DBSCAN clustering algorithms. According to the method, feature vectors are formed by learning space [SNR, distance, angle, speed, micro-Doppler frequency] and other multi-dimensional information, a single threshold value is replaced, and environment features can be captured more comprehensively; and meanwhile, blocks are divided through clustering, false triggering risk levels are marked, a high-risk area is comprehensively judged in combination with a tracking perception algorithm, interference signals such as wall surface vibration, curtain vibration and air conditioner airflow are effectively filtered, and the misjudgment rate is remarkably reduced.

The application relates to the technical field of radars and unmanned aerial vehicles, and particularly discloses a combined radar unmanned aerial vehicle direction finding system. The system comprises at least two radars, a central data processing unit and a direction finding fusion decision unit, and through the cooperation of the multiple radars, target echo signals are collected, multi-dimensional features such as amplitude, micro-Doppler and polarization are extracted, and combined azimuth calculation is carried out based on evidence theory, and finally high-precision direction finding results are output. The system utilizes spatial and frequency diversity gains, combines feature-level to decision-level fusion, and improves the direction finding reliability, environmental adaptability and anti-interference capability.

This invention discloses a weak micro-Doppler detection system for Rydberg atoms using isotropic idler light, belonging to the field of electromagnetic measurement technology. It includes a light pair generation module for acquiring initial light source parameters, injecting pump light and coupling light to generate quantum correlated light pairs; an information loading module for emitting probe light to generate modulated coupling light; a coherent detection module for demodulating the modulated coupling light and isotropic idler light to generate a difference-frequency electrical signal; a parameter tuning module for extracting signal quality indicators and light source quality indicators, tuning the current light source parameters, and generating updated light source parameters; and a spectrum analysis module for analyzing the difference-frequency electrical signal to obtain micro-Doppler characteristics. This invention employs a combined technical solution of quantum correlated light pair generation, Rydberg atom information loading, coherent detection, and closed-loop tuning, which can generate a difference-frequency electrical signal with a high signal-to-noise ratio, and achieve high-sensitivity measurement of the micro-Doppler characteristics of weak targets through spectrum analysis.

The application discloses a wheeled traffic target classification and identification method, system, device and medium based on micro-motion information, and the method comprises the following steps: acquiring a micro-Doppler echo signal of a wheeled target; performing a short-time Fourier time-frequency transformation on the micro-Doppler echo signal to obtain a time-frequency distribution of the micro-Doppler echo signal; calculating an instantaneous frequency of the micro-Doppler echo signal, performing an EMD transformation on the instantaneous frequency, obtaining a plurality of modal components, and selecting a single-frequency component from the plurality of modal components to obtain a wheel rotation period; according to the time-frequency distribution of the micro-Doppler echo signal, the number of flicker points in the wheel rotation period is obtained, and the corresponding maximum speed in the time-frequency distribution is also obtained; and the data set composed of the number of flicker points in the wheel rotation period, the maximum speed and the corresponding vehicle type is input into a support vector machine to classify and identify the vehicle. The application can finely classify and identify different wheeled vehicles.

According to an aspect, there is provided a solution in which sensor data may be obtained from at least one radar sensor arranged in an elevator car. Micro-Doppler signatures of at least one moving object associated with the elevator car may be determined based on the sensor data and the at least one moving object associated with the elevator car may be classified into at least one type based on their micro-Doppler signatures. An elevator associated action may then be performed based on the classifying.

The invention discloses a rotor unmanned aerial vehicle micro-motion parameter estimation method, which comprises the steps of performing pulse compression on radar echoes of a rotor unmanned aerial vehicle, and taking slow time data of a distance envelope peak value position as an observation signal of sparse reconstruction; constructing a dictionary matrix based on observation signals, and converting a micro-Doppler parameter estimation problem into a sparse reconstruction problem; and on the basis of the dictionary matrix, utilizing a Bayesian sparse reconstruction algorithm to estimate the rotation frequencies of a plurality of rotors of the rotor unmanned aerial vehicle. The method is applied to the field of radar signal processing, can perform parameter estimation on a plurality of micro-Doppler signals in an aliasing state in a noise environment, has the advantages of high estimation precision and good robustness, can be widely applied to searching and monitoring of air rotor type unmanned aerial vehicle targets, and has wide application prospects. The method can be effectively suitable for micro-motion parameter estimation of the hovering unmanned aerial vehicle under the low signal-to-noise ratio.

The invention discloses a target micro-Doppler extraction method based on vernier ranging, which belongs to the technical field of radar signal processing, and comprises the following steps: obtaining radar vernier ranging data through first-order and second-order difference; the de-noising effect is verified according to the statistical standard deviation; performing modal decomposition by adopting a CEEMDAN algorithm to obtain an intrinsic mode function matrix and a residual matrix; and Fourier transform is performed on each component, and the frequency corresponding to the maximum amplitude value is extracted as a micro-Doppler frequency value. According to the target micro-Doppler extraction method based on vernier ranging, the high-precision characteristic of vernier ranging and the CEEMDAN anti-aliasing capability are combined, random errors are effectively reduced, the micro-Doppler extraction precision is improved, and the method is adaptive to nonlinear and non-stationary radar signals and is suitable for radar target fine recognition scenes.

This invention relates to the field of target recognition technology, and in particular to a radar target recognition method and system based on micro-Doppler sensing attention. First, a micro-Doppler time-frequency map is generated based on the radar echo signal. Second, the micro-Doppler time-frequency map is input into a lightweight backbone network for feature extraction to obtain an initial spatiotemporal feature map. Third, a dual attention mechanism is used for the first residual compression feature extraction to obtain a first-stage feature map. Then, the first-stage feature map is input into a max-pooling layer for feature downsampling to obtain a downsampled feature map. Next, a dual attention mechanism is used for the second residual compression feature extraction to obtain a second-stage feature map. Finally, the second-stage feature map is input into a classification output module for classification calculation to obtain the radar target recognition result. This invention achieves high-precision target classification under strong background noise by introducing a time-axis integral pooling mechanism and a channel attention mechanism that conform to the physical laws of incoherent accumulation of radar signals.

The application provides a Doppler-based bathroom fall detection method and device and radar, the installation height of the radar is not lower than the installation height of the shower, and the method comprises the following steps: acquiring the track of a target, and acquiring the associated point cloud associated with the track of the target frame by frame, and acquiring the micro-Doppler array of each frame of associated point cloud; determining whether it is a showering scene according to the micro-Doppler array of a continuous first preset number of frames; in the showering scene, acquiring the micro-Doppler array of a continuous second preset number of frames, and calculating the energy Doppler distribution similarity of the associated point cloud of the corresponding second preset number of frames according to the micro-Doppler array of the continuous second preset number of frames; determining the target state according to the energy Doppler distribution similarity of the associated point cloud of the second preset number of frames; and if the target changes from an active state to an inactive state, it is determined that the target falls. The application can improve the target fall discrimination accuracy in the showering scene.

The invention discloses a tracking system and method based on millimeter wave radar end-to-end multi-target detection, and mainly solves the problems of low detection precision and poor tracking stability of pedestrians, non-motor vehicles and motor vehicles in a complex traffic scene in the prior art. The system comprises a radar signal processing unit, a radar detection and identification network and a DeepSORT tracking module. The radar detection and identification network adopts a hybrid architecture comprising an encoder and a decoder, and the encoder comprises a 3D CNN network and an expansion convolution DAM module; the decoder comprises a T-W / SW-MSA module and a full connection layer, according to the DeepSORT tracking module, a radar cross section RCS value and micro-Doppler radar physical characteristics are introduced, cascade matching and confirmation of a new trajectory state are added, so that the stability and robustness of target tracking are improved, and long-term stable tracking of a target is achieved by fusing a target detection result and combining a Hungary algorithm and a Kalman filtering technology. The method is high in detection precision and good in tracking stability, and can be used for intelligent traffic and automatic driving.

The invention discloses an insect flapping frequency inversion method based on initial phase calculation, and relates to the technical field of radar measurement. The method comprises the following steps: establishing an insect flapping motion radar echo model containing an amplitude initial phase and a phase initial phase, and obtaining a micro-motion signal from collected insect echoes based on the insect flapping motion radar echo model to generate a micro-Doppler spectrogram; based on the micro-Doppler spectrogram, calculating an initial phase value of the phase of the insect flapping wing, and based on the initial phase value, constructing a phase compensation factor to obtain a flapping wing parameter plane; and detecting an energy peak value in the flapping parameter plane based on the phase compensation factor, and determining the flapping frequency of the insect based on the position of the energy peak value. According to the method, the phase initial phase and the amplitude initial phase are introduced, the double physical processes of wing movement and body micro movement during insect flapping are restored, a radar echo model is highly matched with a real signal generation mechanism, errors are reduced, and the anti-interference capability is improved.

The invention relates to a quality improvement method for a high-resolution image. The method comprises the following steps: obtaining an equal-target two-way slope distance; calculating a distance error; calculating a two-dimensional frequency spectrum; calculating a truncation phase error; obtaining data of each receiving array element after micro-Doppler phase error compensation; obtaining all receiving array element data after micro-range migration error correction; obtaining data of the class receiving and transmitting combined synthetic aperture sonar; obtaining N data blocks after truncation phase error compensation; obtaining multi-receiving array element synthetic aperture sonar data; filtering to obtain filtered data; obtaining the data after the nonlinear frequency modulation processing; calculating an accurate phase; obtaining a system frequency modulation rate after second-order Taylor series approximation; calculating a coefficient of the expression of the delay time and the distance direction frequency; solving an undetermined coefficient; performing high-order phase error compensation; and a high-resolution synthetic aperture sonar image is obtained. According to the method, the reconstruction performance can be effectively improved under the conditions of larger transmission signal bandwidth and larger accumulated beam angle.

The invention specifically relates to a radar target identification method based on attitude angle division, and the method comprises the steps: obtaining the actual measurement data of a plurality of types of targets, each target sample comprising a time-frequency map, the real-time distance, azimuth angle and pitch angle of a target relative to a radar, and the distance, azimuth angle and pitch angle of the target in a previous time slice; calculating a course angle based on measured data; the method comprises the following steps of: preliminarily setting and dividing angle areas by utilizing a known course angle and a pitch angle on the basis of target micro-Doppler characteristics embodied by a time-frequency spectrum, and constructing a time-frequency spectrum data set of various types of targets in different angle areas; constructing a data set by using convolutional neural network training, then verifying classification precision, and judging whether a neural network model needs to be retrained by continuing an iterative division method or not according to an identification result; and determining a division boundary, training a neural network model, and finally inputting a time-frequency spectrum of a target to finish accurate classification. According to the method, the classification precision of radar target recognition is improved.