1070 results about "Radar signal processing" patented technology

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention belongs to the technical field of radar signal processing, and discloses a radar scattering rapid test method based on polarization decomposition. Comprising the following steps: scanning a target 3D model, simulating scattered field distribution in different incident angles and polarization modes, calculating polarization contrast, and identifying a polarization sensitive area; establishing a dynamic coordinate system conversion model, fusing RTK data, IMU data and laser tracking data, and correcting the position of the trolley and the model in combination with a Doppler frequency shift model and a least square method; calculating an optimal test path of the trolley according to the sensitive area by adopting a natural heuristic optimization algorithm; establishing a geometric environment model, obtaining a back scattering signal through beam forming, analyzing a polarization coherence matrix, obtaining a high-entropy region, and decomposing scattering components; according to the method, closed-loop optimization is formed from a data acquisition source to a scattering component analysis tail end, and the test efficiency and the real-time performance are remarkably improved.

The invention provides a sleep monitoring model training method, a sleep monitoring method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a data set S1 and a data set S2; a pure radar feature extractor M3 is trained by using a pre-trained teacher network model M2 and the data set S1, the teacher network model M2 fuses the radar data and the pulse wave data in the data set S1 and outputs fused feature data, and the pure radar feature extractor M3 performs feature extraction on the radar data in the data set S1 and outputs radar feature data; training a bimodal sleep monitoring model M4 and a pure radar modal sleep monitoring model M5 by using the data set S2, and determining first sleep stage and / or respiratory event information by a first recognition layer according to fusion feature data output by the teacher network model M2, and the second identification layer determines second sleep stage and / or respiratory event information according to the radar feature data output by the pure radar feature extractor M3. According to the invention, the sleep monitoring task is efficiently completed at low cost.

The invention belongs to the technical field of radar signal processing, and discloses a sea surface small target detection method based on optimized characteristic mode decomposition, which comprises the following steps: S1, acquiring to-be-detected signal data; s2, decomposing an original signal into a plurality of modal components by using FMD, and selecting an envelope spectrum entropy as a fitness function; s3, performing global optimization on the fitness function in the FMD by using an SOS algorithm; s4, introducing a PSO algorithm to carry out local optimization on key parameters of the FMD; s5, components with low envelope spectrum entropy values and correlation coefficients larger than a threshold value are reserved; s6, extracting an envelope spectrum entropy and frequency band energy ratio feature from the screened modal components, introducing a Gini coefficient as a weighting factor, and constructing a GSEBE joint feature; and S7, inputting the entropy value of the envelope spectrum into a DELM classifier with a controllable false alarm, and realizing target detection based on comparison between a predicted value and a judgment threshold. According to the invention, the capability of distinguishing sea clutters and target echoes is enhanced, and more accurate classification detection is realized.

The invention discloses a laser radar echo signal distance inversion method based on a deep learning combination model, and belongs to the technical field of laser radar signal processing. According to the method, firstly, a CNN, ResNet or DCNN architecture is adopted to construct a spatial feature extraction module, and local and global spatial features of echo signals are extracted through multi-scale convolution, residual connection and an adaptive denoising mechanism; then, a time sequence dependency relationship of the signals is captured by utilizing BiGRU bidirectional time sequence modeling and a multi-head self-attention mechanism of Transform; and integrating the spatio-temporal features through the feature fusion layer, and then outputting a distance inversion value through the full connection layer. A parallel multi-scale CNN, a shrinkage enhanced residual network and a lightweight DCNN structure are innovatively designed, and the problems of insufficient spatial-temporal feature fusion of a single model and precision degradation under low signal-to-noise ratio interference are solved in combination with dynamic gating adjustment of a bidirectional gating circulation unit and dynamic position coding of Transform. According to the method, the precision and robustness of distance inversion in a complex environment are remarkably improved, and the method can be applied to the fields of automatic driving, environment monitoring and the like.

The invention is suitable for the technical field of radar signal processing, and provides a space target SFM three-dimensional reconstruction method based on a multi-frame ISAR image, and the method comprises the steps: carrying out the preprocessing and imaging of large-angle inverse synthetic aperture radar ISAR echo data of a space target, and obtaining a multi-frame ISAR image sequence; extracting scattering points in each frame of ISAR image sequence according to an orthogonal matching pursuit algorithm, and performing feature matching on adjacent frames of ISAR image sequences to obtain a scattering point set; obtaining instantaneous radar sight line information, and constructing a projection matrix from a space target scattering center three-dimensional coordinate to an ISAR two-dimensional pixel plane according to the instantaneous radar sight line information; and obtaining a three-dimensional reconstruction result according to the scattering point set and the projection matrix in combination with a triangulation principle in an incremental motion recovery structure SFM method. According to the method, the robustness and precision of three-dimensional reconstruction can be improved.

The invention relates to the technical field of computer vision and radar perception, in particular to a ship intelligent detection system based on a convolutional neural network and radar signal processing, which comprises a ship three-dimensional perception modeling module, a visual feature hierarchical fusion module, a target detection module and an anomaly detection module. The system emphatically utilizes deep learning methods such as a convolutional neural network and the like to realize three-dimensional space modeling and visual feature extraction of a ship by a radar in a water area environment. And a layered adaptive fusion and mutual information enhancement mechanism is adopted. The end-to-end detection model introduces a spatial hierarchy weighting strategy, so that the object detection accuracy and interpretability under the conditions of multi-target density, shielding and dynamic change in a complex water scene are improved. The system realizes continuous tracking, anomaly detection and risk early warning of ship navigation behaviors based on space-time dynamic modeling. The whole scheme has high precision, strong robustness and adaptive ability, and can meet the requirements of ship detection and intelligent management and control in complex water area environments such as smart ports and water traffic.

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

The invention discloses a multipath interference suppression method and device during in-pipe detection and a storage medium, and belongs to the technical field of radar signal processing. According to the method, a CFAR detection and clustering algorithm combined method is adopted, and the target number and the center frequency are automatically extracted from a radar amplitude-frequency signal; an original noisy signal is decomposed into a target number of modal components by using an improved variational modal model, and an improved power reference analysis method is proposed to divide the modal components into effective modals and ineffective modals; sVD noise reduction is carried out on the effective mode; and performing signal reconstruction on the plurality of modal components after SVD noise reduction to obtain a signal after multipath interference suppression. According to the method, the problem that the decomposition number and the center frequency of the variational mode decomposition algorithm are difficult to determine is effectively solved, the influence of inaccurate parameters is reduced, multipath interference suppression during complex environment detection is facilitated, and the effectiveness of the algorithm is verified through actual measurement.

The invention relates to the technical field of radar signal processing and quantum computing, in particular to a vehicle-mounted radar signal interference noise reduction method based on a quantum convolution region proposal network. According to the technical scheme, the vehicle-mounted radar signal interference noise reduction method based on the quantum convolution region proposal network can efficiently extract features in a vehicle-mounted radar signal by combining the parallel processing advantages of the quantum convolution region proposal network and quantum calculation, accurately identify and suppress various complex interference signals, and improve the noise reduction performance of the vehicle-mounted radar signal. Self-adaptive processing can be carried out according to the characteristics of different interference signals, it is ensured that the system can cope with variable interference sources, the parallel processing capacity of quantum computing is utilized, rapid and accurate feature extraction and interference suppression can be achieved in the signal processing process, and compared with a traditional high-cost laser radar or visual image recognition system, the system has the advantage that the system is more accurate in feature extraction and interference suppression. On the basis of the radar signal processing method and the advantage of low power consumption of quantum calculation, lower hardware cost and energy consumption can be realized.

The invention discloses a radar adaptive multi-target tracking method under dense clutters, which relates to the technical field of radar signal processing, adaptive filtering and multi-target tracking, and effectively reduces the calculation burden by simplifying a joint probability data association algorithm, reconstructing a confirmation matrix and directly using the confirmation matrix to calculate the association probability. Secondly, a self-adaptive extended Kalman filtering algorithm is introduced, an error covariance matrix is adjusted through a self-adaptive factor, and the tracking stability is improved; and finally, introducing a life cycle to perform target track management, dynamically determining the starting and ending of the track, and accurately tracking the high maneuvering target in the dense clutter environment. The problems of high calculation complexity, poor anti-interference performance and insufficient adaptability to a high-speed maneuvering target can be solved.

The invention discloses a negative group delay system based on SSPP, and belongs to the technical field of microwave communication. A fundamental mode negative group velocity is innovatively generated based on an artificial surface plasmon SSPP, and an SSPP system with negative group delay is realized. The system comprises two parts, namely an amplifier circuit and an SSPP waveguide with a negative group delay unit. The SSPP waveguide with the negative group delay characteristic comprises two coplanar waveguide parts, two transition parts and an SSPP waveguide part, the SSPP waveguide part comprises a unit for generating negative group velocity and a plurality of basic units, and further comprises a microwave dielectric substrate. The amplifier is composed of an amplifier chip, a peripheral capacitor inductor of the amplifier chip and a direct-current power supply. An input signal passes through the system, and advanced transmission of the signal can be realized. The problems of narrow bandwidth, large loss and non-adjustable structure in the prior art are effectively solved, and the antenna has wide application prospects in the fields of 5G communication, radar signal processing, array antenna systems and the like.

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

The invention is suitable for the field of radar signal processing, and provides a multifunctional radar working mode identification method based on a knowledge graph ROTATE model, and the method comprises the steps: randomly generating a radar sample data set according to the preset parameter range and parameter modulation mode of each working mode of a radar; preprocessing the radar sample data set to form a basic unit triple for constructing the knowledge graph; constructing a knowledge graph for describing a relation between a radar working mode and a parameter value and a parameter modulation type based on the basic unit triad; entities and relationships in the knowledge graph are mapped to a low-dimensional continuous vector space for representation, and the confidence coefficient of each basic unit triple is calculated by using a score function defined by an ROTATE model algorithm; and maximizing the confidence coefficient of the basic unit triad to train the model, and performing working mode recognition on the test set by using the trained model. According to the method, the recognition time can be shortened while the high recognition rate is guaranteed, and good generalization is achieved.

The invention discloses a radar target classification method based on a double-flow space-time cross attention map convolutional network, and relates to the technical field of radar signal processing and artificial intelligence cross, and the method comprises the steps: processing an obtained radar echo signal sequence, and obtaining one or more data frame sequences; for any data frame sequence, executing a first operation, specifically, taking each data frame in the target data frame sequence as a graph node, constructing a graph structure model, and determining an adjacent matrix corresponding to the graph structure model; taking the target data frame sequence and the adjacent matrix as input, and determining the probability of each target category monitored by the radar corresponding to the target data frame sequence by using the trained double-flow space-time cross attention map convolutional network; and determining the target category corresponding to the maximum probability as a radar monitoring target category corresponding to the target data frame sequence. According to the invention, the accuracy and anti-interference performance of radar target classification can be improved.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention discloses an airborne radar system and method oriented to a signal transmitting device, and belongs to the technical field of airborne radar signal processing. Received radar task instructions are analyzed, target characteristic parameters, environment constraint conditions and performance indexes are extracted, and mapping relations between task requirements and transmitting waveforms, power and time sequence parameters are established; based on the mapping relation, the transmitting device resources are modeled into a power, frequency spectrum and time sequence three-dimensional state space, and the resource margin and the conflict risk are monitored and predicted in real time; matching a current task situation with a historical strategy library in combination with a resource state, and selecting a historical strategy with the highest similarity as a basic launching scheme; optimizing a resource allocation demand of the basic scheme according to a time sequence and a priority sequence, and introducing a resource buffer mechanism and dynamic priority adjustment to solve a multi-task resource conflict; based on real-time environment data, a basic scheme is decomposed into independently adjustable strategy units, and after optimization and recombination, a transmitting strategy adaptive to a specific scene is generated.

The invention discloses a non-uniform clutter and interference cooperative suppression target fusion detection method and system, and relates to the technical field of broadband radar signal processing, and the method comprises the steps: carrying out the unitary transformation of test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; constructing a distance extension target Gradient detection statistical magnitude under the condition of a known clutter skew symmetry covariance matrix; calculating the maximum likelihood estimation of the clutter skew symmetry covariance matrix; based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the distance expansion target Gradant detection statistic, constructing a target detection statistic of non-uniform clutter and interference cooperative suppression; performing target fusion detection on the target detection unit based on the target detection statistical magnitude and a preset detection threshold; according to the invention, the technical problems of complex construction process and high calculation complexity of the target detector in the prior art are solved.

The invention discloses a satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integration method, equipment and a medium, and relates to the technical field of radar signal processing and radar imaging. The objective of the invention is to solve the problems of inaccurate motion compensation and difficult large-rotation-angle imaging in ISAR imaging. According to the method, a particle swarm optimization algorithm is adopted to optimize motion parameters, optimal motion parameters are obtained, and the motion parameters comprise relative translation motion parameters and relative rotation motion parameters of a target and a radar; and obtaining focusing and calibration imaging results based on the optimal motion parameters and echo data reflected by the target. The influence of the translation compensation effect on rotation parameter estimation is reduced, more accurate parameter estimation and better imaging focusing performance are realized, and the ISAR imaging problem under the condition of violent relative movement in the rendezvous scene is solved.

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

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

The invention provides a high-precision angle estimation method for a distributed millimeter wave radar network, a medium and equipment, and belongs to the technical field of radar signal processing. According to the method, a large-aperture MIMO radar system is constructed based on a plurality of small millimeter wave radars, and an initial rough angle value of a target is obtained by using two-dimensional fast Fourier transform; accurately calculating the walking position of the distance spectrum peak value of each virtual antenna according to the distance resolution of the radar and the rough angle value, extracting all distance spectrum peak value data according to the walking position, and generating a one-dimensional angle vector; performing angle dimension fast Fourier transform on the one-dimensional angle vector to generate an angle spectrum; calculating a target angle estimation value according to the angle spectrum peak value position; the above process is iteratively circulated until the error between two adjacent angle estimation values is smaller than a preset threshold value. According to the method, different types of distributed millimeter wave radar network architectures can be flexibly adapted, the precision problem caused by target distance walking is effectively eliminated, and the angle estimation precision is greatly improved.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

The invention belongs to the technical field of radar signal processing, and particularly relates to a low-confidence small target detection method in radar echoes based on a hybrid architecture, and the method comprises the steps: firstly carrying out the spectrum symmetric movement and dimension recombination of radar echo data, and generating five-dimensional tensors [B, T, C, H, W] containing time sequence features; then the tensor is input into a detection model formed by cascading a Hurglass 3D module and a YOLOv8 network, the Hurglass 3D module extracts multi-scale spatial-temporal features through a structure of three-dimensional convolution down-sampling, bottleneck layer and three-dimensional transposition convolution up-sampling, and feature fusion is achieved through jump connection; and finally, target detection is completed through a backbone network, a neck network and a decoupling detection head of the YOLOv8 network. According to the invention, through spatio-temporal feature combined extraction and small target feature enhancement, the detection accuracy and the positioning precision of the low signal-to-noise ratio small target in radar echoes are effectively improved.

The invention relates to the technical field of radar signal processing, and particularly provides a radar target detection constant false alarm rate control method and system based on sample quantile characteristics, a terminal and a storage medium, and the method comprises the steps: obtaining a radar echo signal of a to-be-detected unit; the method comprises the steps of grouping radar echo signals of a to-be-detected unit, constructing a sample set, and extracting quantile features from grouped samples according to a plurality of preset quantile levels to obtain quantile feature vectors; based on the quantile feature vector, calculating to obtain a statistic of the to-be-tested unit; comparing the statistical magnitude of the to-be-detected unit with a preset judgment threshold value, and if the statistical magnitude is greater than the judgment threshold value, judging that a target exists in the to-be-detected unit; and if the statistical magnitude is smaller than or equal to the judgment threshold value, judging that no target exists in the to-be-detected unit. According to the invention, the target detection sensitivity and the anti-clutter capability are significantly improved.

The invention belongs to the field of radar signal processing, and particularly relates to an improved variable index constant false alarm rate detection algorithm. According to the property of the reference window before and after constant false alarm detection, the corresponding constant false alarm processing method is adaptively selected, and the detection performance of the detector in a multi-target background environment can be effectively improved. Aiming at the situation that a traditional variable index constant false alarm detector (VI-CFAR) has targets in front and back reference windows and the detection performance is poor when a detector selection unit averagely selects a small constant false alarm detector (SO-CFAR), the detector is improved by adopting an adaptive deletion selection small constant false alarm detector (ACSO-CFAR), and when a target exists in a single-side reference window, the detection performance of the detector selection unit is improved by adopting an adaptive deletion selection small constant false alarm detector (ACSO-CFAR). An improved unit average maximum constant false alarm rate detector (CA-CFAR) is selected, and compared with a traditional CA-CFAR, the detection performance is improved under the multi-target background.

The invention discloses a weak radar signal target tracking method, system and device and a medium, and belongs to the field of radar signal processing, and the method comprises the steps: constructing a state space model, predicting a target state and an error covariance at a moment k based on model parameters and a target state filtering value and an error covariance filtering value at a moment k-1, and obtaining a target state and an error covariance; target motion information is obtained according to the echo signals at the moment k, whether dead pixels or leakage points exist or not is judged, if yes, the predicted position value in the target state at the moment k is extracted to replace the original position value, the target position error value and the mean square error are calculated again and replace the original error value and the mean square error, and the target position is obtained. Updating the filtering value into the predicted target state and error covariance at the moment k, and if not, calculating the Kalman gain and combining with the position value at the moment k, and performing filtering correction on the predicted target state and error covariance at the moment k; the system, the equipment and the medium are used for implementing the method. According to the method, the robustness and the tracking precision are improved, and the calculation complexity and calculation resource occupation are reduced.

The invention belongs to the technical field of radar signal processing, and provides a radar signal detection and identification method and device based on deep learning. The method comprises the following steps: performing time-frequency transformation on a time-frequency aliasing radar signal to obtain two-dimensional time-frequency data, drawing a time-frequency graph in a matching manner, combining the time-frequency graph into a mask region of each time-frequency component in the time-frequency graph, and mapping the mask region of the time-frequency graph into the two-dimensional time-frequency data; filtering time-frequency components outside a mask area in the two-dimensional time-frequency data, performing mask filtering on a time-frequency overlapping area to filter an overlapping part, performing time-frequency inverse transformation on the filtered time-frequency data to obtain a time-domain incomplete waveform, and reconstructing a complete radar signal; obtaining a reconstructed radar signal time-frequency diagram; graying processing is carried out, an optimal image segmentation threshold value is determined, a radar signal and a background signal are completely stripped, and a radar signal contour is extracted; and obtaining a modulation mode corresponding to the current radar signal based on the radar signal contour and a pre-trained modulation identification model.

A method for processing a radar signal based on a photonic fractional Fourier transformer comprises: transmitting a linear frequency modulation signal to targets to be detected, receiving echo signals of the targets to be measured, and loading the linear frequency modulation signal and the echo signals onto a single-frequency optical wave by an electro-optical modulator (S1); respectively biasing a sub-modulator and a parent modulator of the electro-optical modulator at different bias points, modulating the single-frequency optical wave by the electro-optical modulator based on the linear frequency modulation signal and the echo signals, and outputting a modulated optical signal (S2); converting the modulated optical signal by a photoelectric detector to a photocurrent (S3); and performing Fourier transform on the photocurrent to obtain a fractional Fourier spectrum, and obtaining distance information of the targets to be measured according to peak positions of each pulse signal in the fractional Fourier spectrum (S4).