126 results about "Micro doppler" patented technology

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 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 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.

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.

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.

The invention belongs to the technical field of gait assessment, and discloses a Parkinson's patient gait assessment method and related equipment, and the method comprises the steps: obtaining a radar point cloud and a micro-Doppler time-frequency spectrum when a user walks in a test area through a millimeter wave radar, and carrying out the preprocessing of the radar point cloud; a human body point cloud cluster is extracted from each frame of radar point cloud after preprocessing through a clustering method, and mass center position tracks of left and right feet are recognized in combination with Kalman filtering and a Hungary algorithm; calculating the speeds of the left and right feet according to the centroid position tracks of the left and right feet, and detecting step events of the left and right feet in combination with the micro-Doppler time-frequency spectrum; calculating gait evaluation parameters according to the time sequence of the gait events and the centroid point position of the human body point cloud cluster; comparing the gait assessment parameters with pre-stored reference data to generate a Parkinson disease assessment result of the user; therefore, continuous and convenient rehabilitation monitoring can be provided for the Parkinson's disease, and the accuracy and practicability of Parkinson's disease assessment are remarkably improved.

This invention relates to the field of smart home appliance technology, specifically to a smart air conditioning control system and method based on a 24GHz millimeter-wave radar. The system includes a 24GHz millimeter-wave radar sensor module integrated into the air conditioner, a signal processing and data analysis module, a main control unit module, an air conditioning execution module, and a feedback module. The method includes: transmitting an FMCW signal via radar and receiving the echo; processing the echo signal to extract distance, speed, and azimuth information of a dynamic human body, and identifying a static human body based on micro-Doppler spectrum analysis; constructing a state model based on this information, including the presence, location, and activity intensity of the person; and generating and executing precise airflow, mode adaptation, and energy-saving control strategies based on this model. This invention solves the problems of traditional sensors being unable to detect stationary human bodies and lacking spatial perception capabilities, achieving all-weather, high-precision, and privacy-protecting proactive intelligent control, significantly improving comfort and energy efficiency.

The invention discloses an airport bird identification and tracking method and system based on visual radar fusion, and the method comprises the steps: carrying out the three-dimensional positioning and morphological analysis of an airport airspace target through the cooperative work of a binocular stereo camera and a millimeter-wave radar; a background difference technology is adopted to extract candidate targets, and multi-dimensional descriptors are constructed through visual features such as morphological asymmetry and wing flapping frequency; performing distance and speed verification on a visual identification result by using micro-Doppler features of radar echoes, and determining a bird identification target through fusion error evaluation; an inter-frame image matching and shielding detection technology is implemented to generate a continuous trajectory sequence, and trajectory fluctuation parameters and periodic features are extracted to reflect flight dynamics; according to the method, identification inconsistency parameters are obtained through consistency checking of multi-frame identification results, bird target identification tracking results are output according to quality grading, and reliable information support is provided for airport airspace safety early warning and bird repelling decision making.

In an embodiment, a method for tracking a target includes: receiving raw data from a millimeter-wave radar, the raw data including a plurality of macro-Doppler frames, each macro-Doppler frame having N chirps, where each macro-Doppler frame stretches over a time interval having a first duration; generating micro-Doppler frames from the plurality of macro-Doppler frames, each micro-Doppler frame including L chirps from M macro-Doppler frames, where each micro-Doppler frame stretches over a time interval having a second duration that is longer than the first duration; detecting one or more moving targets based on the macro-Doppler frames; detecting one or more static targets based on the micro-Doppler frames; and tracking a first target as the target transitions from being detected based on the macro-Doppler frames to being detected based on the micro-Doppler frames.

The application belongs to the field of millimeter wave radar perception and human posture recognition, and discloses a non-contact personnel identity and posture recognition method based on motion instability compensation, comprising the following steps: step 1, static background points are adaptively screened, the radar self-motion speed is estimated, and the point cloud coordinates and the micro-Doppler spectrum are compensated; step 2, a GesAuthNet double-branch deep neural network is built to obtain shape parameters and posture parameters; step 3, an identity authenticator is constructed in combination with the shape parameters to authenticate the legitimacy of the user, and a gesture classifier is constructed in combination with the posture parameters to identify the gesture of the user. The application has the advantages of motion instability compensation capability, robust gesture feature extraction capability, high-precision identity authentication and gesture recognition, and strong stability and robustness.

The invention discloses a deep fusion architecture based on double-helix interleaving directional air-space interference equipment and a gallium nitride technology, and belongs to the technical field of low-altitude defense. The architecture comprises a detection module, an identification module, an interception module and a cooperative control module. The detection module adopts a gallium nitride T / R assembly and a carbon fiber lightweight antenna, and realizes high-maneuverability and high-precision detection in combination with an adaptive beam forming algorithm. And the identification module fuses the micro-Doppler features and the multi-modal data, and realizes high-accuracy target identification through an AI algorithm. The interception module integrates three means of radio frequency, electromagnetic pulse and laser, is driven by an AI grading decision algorithm, and realizes low-cost and efficient damage. And the cooperative control module realizes multi-node resource scheduling and cooperative combat through distributed networking and semi-autonomous AI decision. The problems that a traditional low-altitude defense system is poor in maneuverability, low in recognition rate, high in interception cost and weak in cooperative capacity are solved, and the method is suitable for regional protection of unmanned aerial vehicle clusters and low-altitude high-speed targets.

The application provides a radar signal deep feature extraction method and system based on an adversarial sample defense, an electronic device and a storage medium, and relates to the technical field of radar signal processing. The application first collects a spacecraft synthetic aperture radar original echo signal, converts it into a time-frequency feature map, and extracts intra-pulse and inter-pulse features in parallel to generate a combination matrix. Then, a deception jamming signal is received by a polarization radar group, and a signal data body without polarization influence is generated through complex coherent superposition. Both are input into a feature decoupling adversarial network to separate the warhead micro-motion target feature data by maximizing mutual information constraints. Finally, the multi-scale local attention module is input to extract multi-band feature components, the adaptive weight coefficients are fused according to the power entropy, the polarization channel energy is corrected, and the anti-jamming fingerprint feature with micro-Doppler feature enhancement is output. The spacecraft synthetic aperture radar signal can be processed to resist interference, and the warhead target anti-jamming fingerprint feature with micro-Doppler feature enhancement can be effectively extracted.

The application discloses a multi-modal human body recognition system and method based on photoelectric super surface and radar fusion, and belongs to the field of optoelectronics and radar sensing technology. The system realizes optical edge enhancement through a phase programmable super surface, combines continuous wave radar and frequency modulated continuous wave laser radar to obtain micro-Doppler spectrum and three-dimensional point cloud, and simultaneously collects near-infrared reflectance spectrum. On the algorithm, a cross-modal Transformer-GAT architecture is adopted to fuse multi-source features, an adaptive kernel function is used to optimize edge extraction, and a Mach-Zehnder interferometer or a ReRAM chip is used to realize low-power edge reasoning. Experiments show that the recognition accuracy of the application in complex scenes such as multi-target occlusion and low light is significantly better than that of a single modal scheme, and the application has edge deployment capability of ≤25ms delay and ≤4W power consumption, thereby providing an efficient solution for multi-scene human body dynamic recognition and health monitoring.

The invention particularly relates to a radar micro-Doppler spectrum generation method for classification and identification, and the method comprises the steps: receiving an input time domain signal, and carrying out the Fourier transformation of the input time domain signal; rearranging frequency spectrum components obtained by Fourier transform according to an amplitude descending order; setting a spectrum distance threshold value and an intensity threshold value, and gradually eliminating fuselage components through the steps of primary screening, secondary screening and iterative elimination; recovering the frequency spectrum component without the fuselage component to an initial frequency position according to an original frequency index, and executing inverse Fourier transform to obtain a pure time domain signal; and performing time-frequency analysis on the pure time-domain signal by using S transformation to generate a time-frequency matrix, and normalizing an amplitude value of the time-frequency matrix to a [0, 255] gray scale interval to obtain a radar micro-Doppler spectrum. According to the method, a fuselage component elimination method is improved, S transformation with higher time-frequency resolution is selected, a time-frequency atlas data set fully reflecting the micro-Doppler characteristics of the target can be generated, and the method can be used for a convolutional neural network to realize accurate classification and recognition.

The invention discloses a radar human body activity identification spectrum diffusion generation method based on a dual-attribute condition, and belongs to the technical field of crossing of radar perception and multi-modal artificial intelligence. According to the method, natural language semantic prompt and a condition diffusion generation framework are combined, and two semantic conditions of'character identity 'and'action category' are simultaneously introduced in a generation process by utilizing a cross-modal semantic alignment and hierarchical semantic modulation technology; and a micro-Doppler radar spectrogram with a real time-frequency structure and semantic consistency can be accurately synthesized according to semantics. The method provided by the invention can be used for automatic generation and data enhancement of high-quality pseudo actual measurement data, and supports training, verification and robustness evaluation of a downstream human body activity recognition model, so that the actual measurement data acquisition and labeling cost is remarkably reduced, the recognition precision and generalization ability are improved, and the method is suitable for popularization and application. And actual deployment and popularization of application scenes such as radar-based intelligent security and protection, smart home, remote health monitoring and the like are promoted.

The invention relates to an unmanned aerial vehicle radar detection and identification method and system based on background contrast attention and double-flow micro-Doppler fusion, and belongs to the technical field of radar signal processing and intelligent target identification. According to the method, the problems of low unmanned aerial vehicle detection rate, high false alarm rate, and poor recognition robustness caused by observation angle change and sample scarcity in a low signal-to-noise ratio environment are solved. According to the technical scheme, the method comprises the steps of performing pulse compression on a radar signal to construct a distance Doppler map, realizing rapid positioning of a target by using a full convolution detection network integrated with background contrast attention, generating a time Doppler map and a time cepstrum map, and performing few-sample learning recognition through a double-flow feature fusion network based on Transform. According to the method, the detection precision can be remarkably improved, the recognition robustness is enhanced, the real-time efficiency is optimized, and the method adapts to a few-sample condition.

The application discloses a gait recognition method and system based on an FMCW millimeter wave radar and a storage medium, and the method comprises the following steps: constructing a millimeter wave radar echo data matrix; extracting a micro-Doppler time-frequency spectrum image feature of the data matrix; preprocessing the micro-Doppler time-frequency spectrum image; performing image segmentation on the micro-Doppler time-frequency spectrum; training data using a clustering algorithm; and performing gait recognition according to a clustering result. The application solves the problems of low spectrum image accuracy caused by millimeter wave radar signal noise and deep learning overfitting in related technologies.

The present application relates to radar target detection and identification technical field, especially in kind, a kind of dual-rotor micro-doppler feature extraction method and system, first to the echo sequence of dual-rotor unmanned aerial vehicle carries out the micro short-time Fourier transform, obtains initial amplitude spectrum matrix;Secondly, according to initial amplitude spectrum matrix, calculate the gentle loss function of party damage;Then using gradient descent method, according to the window length of time window function in gentle party damage loss function adaptive closed loop iteration optimization, output the optimal window length of time window function;Finally, according to the optimal window length, determine the time-frequency feature map spectrum.The present application parameterizes and differentiable time-frequency transform process, uses gradient descent adaptive to find optimal window length, does not change analytical bandwidth with frequency, but is matched with global optimal bandwidth to target characteristic frequency, solves the problem that resolution and cross term cannot be considered in traditional method in dual-rotor signal processing.

The invention provides a radar echo real-time simulation method based on an FPGA and a digital array, and relates to the technical field of radar system simulation and testing, and the method comprises the following steps: receiving a radar transmission signal, and carrying out the digitalization and down-conversion processing, and obtaining a baseband I / Q signal; in the FPGA, performing multi-scattering-point range profile generation processing on the baseband I / Q signal to form a plurality of echo components corresponding to different scattering points of a target; performing micro-motion characteristic modulation processing on the plurality of echo components: sequentially performing main body Doppler modulation and micro-Doppler modulation on each echo component through a multi-stage phase modulation unit so as to generate an echo signal with main body motion and micro-motion characteristics; carrying out superposition synthesis on the echo signal subjected to micro-motion characteristic modulation and a simulated environmental effect signal to obtain a synthesized signal; and performing up-conversion and digital-to-analog conversion on the synthesized signal, and outputting the signal to a tested radar through a digital array feed system.