224 results about "Micro doppler" patented technology

The invention discloses a multi-source federal cross-domain and source-domain enhanced millimeter wave action recognition method and system, and the method comprises the steps: generating a micro-Doppler spectrogram through a millimeter wave radar, and extracting the motion features of human body actions through a signal processing module; in the federal multi-source domain adaptation module, dynamically evaluating and fusing knowledge of a plurality of source domains by adopting a voting-based pseudo-tag method and a weighted knowledge aggregation mechanism, and optimizing the generalization ability of a target model; and through a generalization gap optimization method, the performance of the source domain model is improved, and the robustness of the system in different environments is ensured. Through combination of a federated learning framework and a multi-source domain adaptation technology, unsupervised learning under the condition that a target domain has no annotated data is realized, only a single set of millimeter wave equipment is needed, a millimeter wave communication protocol is compatible, and the method has the characteristics of privacy protection, unsupervised learning, multi-source knowledge fusion and strong generalization ability. The method is suitable for application scenes of smart home, health monitoring, man-machine interaction and the like, and has wide practical application value and research prospect.

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 discloses a human body posture intelligent identification method based on a multi-modal micro-motion feature and a Transform network, and the method comprises the steps: deducing micro-Doppler signal mathematical analysis expressions corresponding to different parts of a human body from human body target radar echo modeling; the method comprises the following steps: performing data preprocessing on target echo data acquired by a millimeter wave radar by adopting two-dimensional fast Fourier transform to obtain a one-dimensional spectrum sequence and a two-dimensional Doppler image corresponding to different postures, and respectively sending the one-dimensional spectrum sequence and the two-dimensional Doppler image into a one-dimensional Transform network and a two-dimensional Transform network for feature extraction; and carrying out feature splicing by adopting a multi-modal feature fusion model and outputting a classification result. According to the method, the problems of low recognition rate, poor robustness and the like in the existing human body posture intelligent recognition means can be solved.

The invention discloses an activity identification system and method based on millimeter wave radar Doppler characteristics, and relates to the technical field of radar signal processing and artificial intelligence. Comprising a human body behavior radar acquisition module, a human body behavior information transmission module, a human body behavior information preprocessing module, a micro-Doppler feature extraction module, a data classification and identification module and a human body behavior information application module which are connected in sequence, a millimeter-wave radar is adopted to emit high-frequency millimeter-wave signals, and information of human body actions is obtained by receiving signals reflected from the surface of a human body; and the human body behavior information transmission module is used for transmitting the collected behavior data of the user to edge equipment or a cloud server.

The invention provides a millimeter wave radar waveform data processing method and system, and belongs to the technical field of radar data processing, and the method comprises the steps: carrying out the time-frequency analysis of a plurality of waveform arrays, and obtaining a plurality of image groups and a plurality of micro-Doppler features; the plurality of waveform arrays are obtained by monitoring a target area based on a plurality of millimeter wave radars; generating a target identity label based on the target motion information and the target micro-Doppler feature; the target motion information is obtained by identifying a target person in a target area based on a plurality of image groups, and the target micro-Doppler feature is obtained by calculating based on a plurality of micro-Doppler features; and performing trajectory tracking on the target person based on the target identity identifier. According to the millimeter wave radar waveform data processing method and system provided by the invention, the waveform data of the millimeter wave radar can be analyzed, and the accuracy of personnel trajectory tracking is improved.

The invention relates to the technical field of human body breathing micro-motion modeling and holographic imaging, and particularly discloses a radar human body breathing micro-motion 4D modeling and high-resolution holographic imaging method, which is characterized by comprising the following steps of: performing irradiation perception on a human chest target by adopting a multiple-input multiple-output plane radar array; a thoracic cavity target is regarded as a'vector signal source ', namely the thoracic cavity target is equivalent to a set of scattering points of which the spatial positions are mutually discrete; in the geometric model, a graphical representation 3D space coordinate system is established by taking the center of a radar array as an original point and a plane where the array is located as an XOZ plane. According to the radar human body breathing micro-motion 4D modeling and high-resolution holographic imaging method, the micro-Doppler principle is combined, and the micro-Doppler information of each scattering point in the fourth dimension, namely the slow time dimension, is obtained based on the phase decomposition and extraction technology, so that 4D precise reduction of the fluctuation process of the thoracic cavity of the human body is realized.

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.

A computer-implemented method includes receiving environment description data associated with a simulated environment including an object and identifying an object type of the object. The method includes, in response to the object type being a human, identifying a motion type associated with the object, loading a CAD model associated with the object, loading a motion file associated with the motion type, and mapping the CAD model to the motion file. The method includes performing ray tracing simulation, performing physical optics simulation, calculating at least one of a Doppler shift and a micro-Doppler shift for each ray of a set of rays, performing ray clustering, and transforming a simulation output for display on a user device. The simulation output includes a motion simulation associated with the motion type of the object. The motion simulation includes a main motion of the object and a set of micro-motions of the object.

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.

Systems and techniques are provided for efficient joint communications and radio frequency (RF) sensing. For example, a method for communications and sensing can include receiving a first signal based on a reflection from a target and generating a frame of Doppler spectrum based on the first signal, wherein the frame of Doppler spectrum includes one or more Doppler-domain characteristics for identification of the target. A micro-Doppler measurement report can be generated based on the frame of Doppler spectrum, wherein the micro-Doppler measurement report includes one or more compressed portions of the frame of Doppler spectrum.

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-modal millimeter wave gait recognition method based on multi-scale attention, and the method comprises the steps: obtaining three-dimensional point cloud data and micro-Doppler spectrum data of a pedestrian at the same time through a millimeter wave radar, and carrying out the preprocessing operation; performing multi-scale feature extraction on the preprocessed three-dimensional point cloud data by using point cloud branches, and performing micro-Doppler multi-scale feature extraction on the micro-Doppler spectrum data by using micro-Doppler branches; features output by the point cloud branches and the micro-Doppler branches at different scales are combined through linear projection and attention weighting, and multi-frame gait sequence features are obtained; and inputting the multi-frame gait sequence features into a bidirectional long-short-term memory network to perform modeling of a time dimension, paying attention to key moment action features through a time attention mechanism, and outputting a pedestrian gait recognition result by adopting a classifier. The method has the advantages of strong privacy protection, high recognition precision and strong robustness, and is suitable for the fields of security monitoring, smart home, medical health and protection and the like.

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 present invention discloses an indoor fall detection method based on millimeter-wave radar, which acquires a dynamic point cloud set and a tracking target set; records the height of the only target in the room in a height array, calculates the difference between the last data in the height array and the mean value of the first M data in the height array. If the height difference is less than the height difference threshold, and the height of the current frame is less than the lowest height threshold, and the point cloud broadening in different directions is less than the respective direction thresholds, then increment the fall state counter by 1; otherwise, assign 0 to the fall state counter and set all the values in the height array to 0; when the fall state counter is greater than the threshold, it is preliminarily determined that a fall has occurred; use STFT to analyze the time-frequency characteristics of the fall position, and reconfirm the fall state through the maximum Doppler frequency shift characteristic. The present invention combines the height change information of the point cloud trajectory and the micro-Doppler characteristics at different time periods in the time domain, which can avoid interference problems and quickly and accurately judge the fall state of the personnel target.

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.