109 results about "Radar radiation" patented technology

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

The invention relates to the technical field of radar electronic countermeasures, and particularly discloses a radar radiation source open set identification method (ARPLAD) based on countermeasure reciprocity point learning. The method comprises the steps that adaptive noise reduction and feature extraction are conducted on radar signals through a feature extraction network fusing a DRSN module and an ECA module, channel-level adaptive noise reduction is achieved through the DRSN by means of a soft threshold function, and key features are strengthened through dynamic weight distribution by means of the ECA; an adversarial reciprocity point learning framework is adopted, reciprocity points are set for each known class to serve as out-of-class space representation, and known and unknown class feature spaces are separated by maximizing the distance between known class samples and the corresponding reciprocity points; introducing a central loss function to compress intra-class feature distribution, and constructing a weighted total loss function optimization network in combination with an open space limitation function; and calculating a class self-adaptive threshold value based on the training sample, and comparing the maximum distance from the sample to each reciprocity point with the threshold value during testing to judge known and unknown classes.

The invention provides a radar working mode recognition method, device and equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a pulse description word (PDW) sequence of a radar radiation source; based on the PDW sequence, determining intra-pulse features of the radar radiation source and inter-pulse features of the radar radiation source; inputting the intra-pulse feature and the inter-pulse feature into a working mode feature extraction model to obtain an intra-pulse feature vector and an inter-pulse feature vector output by the working mode feature extraction model; and inputting a feature fusion vector of the intra-pulse feature vector and the inter-pulse feature vector into a radar working mode recognition model to obtain a radar working mode recognition result output by the radar working mode recognition model. According to the method, the intra-pulse features and the inter-pulse features are obtained firstly, then the intra-pulse features and the inter-pulse features are subjected to feature extraction, the intra-pulse feature vectors and the inter-pulse feature vectors with context information are obtained, and therefore high-precision recognition of the radar working mode can be achieved for all scenes.

The invention discloses a radar radiation source identification method based on zero sample learning, a storage medium and equipment, and the method comprises the steps: firstly obtaining an effective pulse of a radar radiation source signal, and training a one-dimensional convolutional neural network containing an auto-encoder based on the effective pulse of a known signal; the method comprises the following steps: selecting an optimal discrimination channel which is most effective for category discrimination by analyzing initial feature space distribution, determining a sparse region of known category signal distribution along the channel as a rejection interval, and identifying an unknown signal; then, unknown category signals are combined, and an enhanced open set classifier capable of explicitly distinguishing unknown categories is trained; and finally, integrating the initial model and the enhanced open set classifier, and identifying an unknown category and classifying a known category through a confidence-based fusion decision rule. According to the method, the detection capability of the unknown radar radiation source can be learned from known data without a prior sample of the unknown radar radiation source, and the identification problem of the unknown radar radiation source in the open environment is effectively solved.

The invention relates to the technical field of echo signal analysis, and discloses a radar radiation source signal identification method based on multi-task learning under physical feature constraint, which comprises the following steps of: 1, establishing a data set by a two-dimensional tensor and a physical feature tag; 2, the identification model comprises a feature extraction network and a multi-task learning network, and the multi-task learning network comprises a frame prediction branch, a classification branch, a parameter regression branch and a frequency spectrum prediction branch; 3, training the recognition model to obtain a trained recognition model; and step 4, inputting a two-dimensional tensor to be recognized into the trained recognition model, and outputting predicted physical characteristics, probability distribution of signal categories, predicted values of parameters and a remodeled two-dimensional STFT spectrogram. Physical priori knowledge and multi-task cooperative constraint are introduced, the physical nature of radar signals can be accurately captured, the interpretability and anti-noise capability of the model are improved, the expression capability of the model for key features is enhanced, and the method is suitable for scenes with sparse feature distribution or weak target signals.

The invention provides a radar model identification method and device and a storage medium, and belongs to the technical field of radar identification, and the method comprises the steps: obtaining original pulse description word data and original intermediate frequency data through electronic reconnaissance collection equipment; associating the original pulse description word data with the original intermediate frequency data to obtain associated pulse description word data and associated intermediate frequency data; and performing parameter analysis on the associated pulse description word data and the associated intermediate frequency data to obtain a target parameter group. According to the method, the fault tolerance is improved, the method can be used for identifying the model of the radar radiation source when signals are missing or distorted, the identification rate of the radar model is greatly improved, and meanwhile, the method does not need to depend on the type of radar radiation source signals in a database and the quality of collected parameters.

This application relates to the field of individual radiation source identification, specifically to a method and apparatus for individual radar radiation source identification in small-sample open-set scenarios. One specific implementation of the method includes: inputting the acquired target input signal into a pre-trained optimized feature extraction model to obtain the original target activation vector; determining the target activation distance vector based on the original target activation vector; inputting the target activation distance vector into a preset extreme value theoretical model to obtain a target reliability score; correcting the original target activation vector based on the target reliability score to generate a corrected target activation vector; normalizing the corrected target activation vector based on a preset normalization formula to generate a normalized probability; and determining target identification information based on the normalized probability, a preset threshold, and a preset identification formula. This implementation can achieve individual radiation source identification under small-sample conditions.

The application discloses a kind of based on transfer learning's radiation source individual identification method, equipment and medium, belong to the field of radiation source individual identification in electronic reconnaissance, including steps: S1, obtains radar radiation source individual time-frequency spectrum;S2, constructs radar individual identification model based on transfer learning;S3, utilizes the model of step S2 and is constructed to complete radiation source individual identification.The application improves the identification ability to radiation source individual.

The invention relates to a radar sensor (10) having at least one antenna element (20-22, 25-27) and a converging radar lens (30); a focal point (40) of the converging radar lens for radar radiation has a predetermined focal length (FA) from a lens side (31) of the converging radar lens (30) facing the focal point (40). According to the invention, at least one antenna element (20-22, 25-27) is spatially arranged outside an area located between the focal point (40) and a lens side (31) of the converging radar lens (30) facing the focal point (40).

A radar device. The radar device includes a transceiver apparatus that comprises at least three transmit antennas and at least three receive antennas or comprises at least two transmit antennas and at least two receive antennas having two-dimensional beam forming, wherein the transceiver apparatus is configured to emit radar radiation using the transmit antennas, to receive radar radiation using the receive antennas, and to generate radar data on the basis of the received radar radiation. The radar device further comprises an evaluation apparatus that is configured to establish whether radar radiation has propagated between the transceiver apparatus and the at least one target either directly or at least partly by way of at least one reflection by evaluating the radar data using a multitarget angle estimation model, wherein the multitarget angle estimation model takes the propagation of radar radiation along at least four paths into consideration.

The present invention provides a method for identifying individual radar radiation sources based on deep learning, which relates to the field of communication identification technology. The present invention forms a complex signal and a residual signal according to the electromagnetic signal obtained by the radar, inputs the complex signal into a CNN network to train a recognition model and obtains the confidence in the recognition model. The residual signal is processed in the same way as the complex signal to obtain an energy spectrum of the residual signal, and the energy index in the energy spectrum is matched with the confidence in the recognition model. The sub-region in which the energy index has a definite relationship with the confidence and the corresponding influencing factor are obtained, and the confidence in the recognition model is corrected by the influencing factor, so as to improve the judgment ability of the recognition model by utilizing the influence of the residual signal on the confidence.

Seeker head (2) for a guided missile (1), comprising a detection device (3) having a first detection unit (6) for detecting infrared radiation and a second detection unit (8) for detecting radar radiation, wherein the detection unit (3) has a cooling device (14) configured to cool at least a first detection element (7) of the first detection unit (6), in particular a detector, and at least a second detection element (9) of the second detection unit (8), in particular an antenna element.

The invention discloses a radiation source individual identification method based on a liquid neural network, and the method comprises the steps: firstly carrying out the preprocessing of a radar radiation source individual identification sample set, extracting a transient signal in the sample set, then extracting the diagonal bispectrum features in the sample set, dividing a training set and a test set, and marking an individual label for each individual; constructing a liquid neural network model as a radiation source individual identification model, training the model by adopting a training set, calculating classification loss by utilizing a cross entropy loss function, and obtaining the radiation source individual identification model through back propagation of model weight and training; and finally, inputting a test set into the trained model for prediction and judgment, and completing the effect evaluation of radar radiation source individual recognition. According to the method, the dynamic calculation capability of the liquid neural network is fully utilized, the time sequence characteristics of the signals can be effectively captured, the radar radiation source individuals are accurately identified, and the method has a good application prospect.

The application provides a radar waveform classification method and system based on effective area identification, and the method comprises the following steps: inputting a radar emitter time-frequency image into a preset SSD network trained in advance to obtain an effective area where a single intra-pulse signal is located and a corresponding radar waveform category output by the preset SSD network; the preset SSD network is trained by taking multiple radar emitter time-frequency images as training samples, taking the effective area where the single intra-pulse signal is located in each radar emitter time-frequency image and the corresponding radar waveform category as labels. The application identifies the effective area where the intra-pulse signal of the radar emitter time-frequency image is located by using the SSD network, and only classifies the category of the waveform in the effective area in the subsequent process, thereby avoiding the influence of signal characteristics in the invalid area on identification, and effectively improving the identification accuracy of the radar waveform.

The invention relates to an angle-resolved radar sensor (8), comprising a transmitting and receiving device and an evaluation device (32) for determining positioning data of a target (24) reflecting radar radiation, the evaluation device (32) being configured to identify a multi-path scene in which the radar radiation is reflected by the target (24). A portion of the received signal representative of the target is based on the reflection of the transmitted and / or received signal at the reflection surface (42), characterized in that the evaluation device (32) is further configured to automatically determine the azimuth angle (gamma) of the reflection surface (42) or to read it from an external source, and determining a tangential velocity (v [theta]) of the target (24) based on the different Doppler shifts of the signal portions obtained on the different paths.

The invention relates to a method (100) for reconstructing elevation information (2a) from measurement data (2), which is recorded with at least one radar device (1) and contains a two-dimensional spatial distribution (3) of at least one physical measurement variable, wherein the measurement data (2) is delivered (110) as an input variable (5) to at least one generator module (4) configured as a neural network, and wherein at least one output variable (6) is called up (120) from the generator module (4), which is a measure of how great an elevation angle (6a) at which radar radiation is reflected by at least one object to the radar device (1). A method (200) for training a generator module (4) for use in the method (100) is also described. A method (300) with a complete chain of actions up to the maneuvering of a vehicle (50) is also described.

The invention relates to a method and an assembly (11) for calibrating at least one radar sensor (25) of a vehicle (30), the assembly (11) comprising a calibration device (1) which has: - a reflector plate (10) for reflecting radar radiation from the radar sensor (25), and - at least one camera (60), and the assembly (11) being designed to acquire orientation information relating to at least one of the vehicle (30), the radar sensor (25) and the reflector plate (10) on the basis of image information captured by the camera (60) and to generate, on the basis of this orientation information, control specifications for the actuator-based movement of the reflector plate (10).

The invention discloses a PRI sorting method and system in a complex electromagnetic environment, and the method specifically comprises the steps: carrying out the sorting and extraction of a pulse sequence through employing a dynamic two-dimensional feature vector matching algorithm, and obtaining a pulse sequence of a fixed and staggered PRI modulation signal; then carrying out sorting extraction on the residual pulses by using a square sine interpolation algorithm to obtain a pulse sequence of the jitter PRI modulation signal; then, through a feature recognition method, obtaining modulation features of the pulse sequences of the fixed and staggered PRI modulation signals and the pulse sequences of the jitter PRI modulation signals; and finally, carrying out intra-frame blending on the sorting result of the pulse stream, and carrying out inter-frame blending on the sorting result of the previous pulse stream to finish sorting. According to the method, the PRI sorting accuracy is improved, the batch increasing phenomenon is reduced, multiple PRI modulation patterns can be sorted and recognized, the pulse sequences belonging to different radar radiation sources are separated from a large number of mixed radar signals, and a reliable data basis is provided for subsequent recognition, positioning and confrontation.

The present invention discloses a multi-radar radiation source sorting and identification method based on hierarchical clustering and time-frequency convolution, which can solve the intra-class clustering and inter-class aliasing phenomena, and efficiently complete multi-category sorting and identification. Compared with traditional sorting algorithms, it can effectively solve the problems of intra-class feature distribution and inter-class feature aliasing, and the processing speed of the sorting algorithm is improved. In view of the problems of high overlap of inter-class feature distribution range and poor separability of original PDW features, periodic features between different categories are extracted based on slow time dimension TFA, which improves the recognition effect.

Radar data fusion evaluation for classifying objects. Method (100) for classifying an object (4) from measurement data (2) recorded with at least one radar sensor (1), with the steps: • providing a spectrum (5) of time-dependent measurement data (2) of the radar sensor (1) (110); • determining positions (6) from the spectrum (5) (120), from which positions radar radiation reflected reaches the radar sensor (1); • determining at least one group (7) of such positions (6*) belonging to one and the same object (4) (130); • determining for each position (6*) in the group (7) a component (5*) of the spectrum (5) (140), which corresponds to the radar radiation reflected by the position (6*); • aggregating (150) all these components (5*) for the object (4) and delivering to a classifier (8) (160); • assigning the object (4) with the classifier (8) to one or more classes (3a-3c) of a predefined classification (3) (170).

The embodiment of the invention discloses a variable modulation radar signal individual identification method and device. A specific implementation mode of the method comprises the following steps: acquiring a variable modulation radar signal emitted by a radar radiation source individual; individual identification is carried out on the variable modulation radar signal through a preset radar signal identification model to generate a radar signal individual identification result, and the radar signal identification model comprises a feature extraction module, a deep variational auto-encoder, a label prediction module and a domain classification module. The feature extraction module is used for extracting fingerprint features of the variable modulation radar signals, and the domain classification module is used for judging a source domain or a target domain corresponding to the variable modulation radar signals. According to the embodiment, the accuracy of radar signal identification can be improved.

The application discloses a radar emitter hierarchical identification method based on continuous learning, and specifically comprises the following steps: firstly, envelope analysis and bispectrum analysis are performed on radar signals; a model identification network and a plurality of individual identification networks under each model are trained according to model tags and individual tags; and a'model-individual' hierarchical identification method is used to identify the radar model to which the data belongs, and then identify the radar individual to which the data belongs in the corresponding model. When the number of radar individuals is large, the accuracy of the method is much higher than that of a single individual identification network method. The training process of the network is performed in a continuous learning mode, specifically, a new model and an old model are used to extract feature vectors and calculate class embedding vectors of known categories of data, cosine similarity is used as a measurement, and the difference between the new and old vectors is reduced, so that the model can learn new category data while ensuring that the identification ability of the model for old category individuals does not decrease.

The invention relates to a lighting device (1) for visibly displaying at least one ornament (7), in particular for a motor vehicle, comprising a plurality of light units (3, 5) having a light source (11) for generating light and at least one light exit area (13), wherein at least one light-transparent cover element (17) encloses the at least one light exit area (13), wherein the light-transparent cover element (17) is arranged outside a radar coupling-in surface (19) of at least one radar-transparent section (9) for coupling-in radar radiation with respect to an optical axis (A) of the lighting device (1), and at least one radar-transparent section (9) having a radar coupling-in surface (19) for coupling-in radar radiation.

The invention discloses an improved WaveGAN radar radiation source sequence signal augmentation method based on physical constraints, and relates to the technical field of radar radiation source signal processing, and the method comprises the steps: carrying out the preprocessing of an original radar time domain signal, and constructing a one-dimensional radar signal sequence data set; an improved WaveGAN model is constructed, a generator of the improved WaveGAN model adopts a deep deconvolution neural network containing a pyramid type multi-scale convolution kernel and introduces a self-attention mechanism, and a discriminator adopts a convolution neural network containing an inverted pyramid type multi-scale convolution kernel and introduces a self-attention mechanism; inputting a data set into the model for adversarial training, wherein a total loss function of a generator is formed by weighting adversarial loss and mean square error loss of constraint signal physical characteristics; and synthesizing a radar radiation source signal sequence for data augmentation by using the trained generator. According to the method, the quality of the generated signal is remarkably improved.

The application discloses a radar anti-reconnaissance signal design method based on a clutter, and implementation steps are as follows: a radar signal is radiated by a radar of a home side; a baseband clutter of a Weibull distribution is generated by a protection node; the clutter is modulated to the received radar signal by the protection node, a main lobe of the protection signal is aimed at a reconnaissance receiver of a blue side, and a side lobe is aimed at a side lobe of the radar of the home side; the radar of the home side suppresses the clutter in the side lobe of the received signal through a moving target display technology; and the main lobe of the clutter protection signal interferes with a front-end detection performance of the reconnaissance receiver of the blue side. Through utilization of the clutter, the clutter is utilized to destroy the front-end detection performance of the reconnaissance system of the blue side, the radar of the home side adopts the moving target display technology to eliminate the influence of the clutter on the radar of the home side, so that the problem that active jamming causes interference to the radar of the home side is solved.

This invention proposes a method and apparatus for synthesizing array radar radiation patterns using space-time joint modulation. The method includes: determining initial values ​​of the array radar element excitation coefficients; acquiring the array far-field radiation pattern under the influence of the initial values ​​of the element excitation coefficients; dividing the array far-field radiation pattern into main lobe and side lobe regions based on its characteristics; setting the random modulation phase of the main lobe region to zero, maintaining the same main lobe phase across different pulses, while modulating the phase of the side lobe region regularly or randomly, while keeping the array far-field radiation pattern amplitude constant, thus obtaining array radiation patterns with different phase characteristics under multiple pulses; performing Fourier transforms on the array radiation patterns under different pulses to obtain the space-time two-dimensional element excitation coefficients; and applying the space-time two-dimensional element excitation coefficients to the array radar system to synthesize the array radar radiation pattern after space-time joint modulation. This invention significantly improves the array radar's resistance to sidelobe clutter and interference.

The invention belongs to the technical field of radar radiation source identification. The invention provides a radar radiation source identification method based on relation perception and prototype optimization. According to the embodiment of the invention, through channel gating and a soft attention mechanism, invalid frequency band features are automatically inhibited, and the weight of a bad sample which deviates from the center is reduced. And a GCN is introduced to construct a sparse prototype graph, so that the model can perceive a similarity relationship between categories, and local consistency is enhanced through a graph propagation mechanism. And in cooperation with prototype comparison loss, centers of similar categories are forcibly pushed away, so that a clearer isolation strip is formed at a decision boundary, and the false identification rate is remarkably reduced. A residual mean fusion structure is also designed. When an attention mechanism may fail due to extremely small sample size, a mean value prototype on a residual path can play a role in guaranteeing the bottom, so that the stability of model training is ensured; and when the sample size is slightly large, the attention mechanism can provide finer feature expression, so that the universality of the model under different small sample settings is realized.

The invention discloses an available power time-varying radar radiation power distribution method. The method comprises the following steps: acquiring the currently available total power of a radar in real time; determining available radiation power of the radar from the currently available total power; according to the pulse width, the pulse repetition period and the task execution data rate of each radar task in the current scheduling interval, calculating the average power of each task; in the current working mode, the priority of each task is analyzed; gradually adjusting the pulse width or the data rate according to the task priority; the tasks to be executed by the radar are arranged according to the scheduling interval, the power consumption in the current scheduling interval is counted in real time, and if the instantaneous power exceeds the available radiation power, the low-priority tasks are added into a delay list; and calculating the detection capability of a specific radar cross section (RCS) target under the current power in real time. According to the invention, available power fluctuation can be responded in real time, radar emission resources are dynamically optimized, and the execution success rate of high-priority tasks is preferentially guaranteed.

The utility model provides radar module equipment and terminal equipment based on limited clearance. The radar module equipment comprises an antenna unit, a radar module bottom plate, at least one coupling supporting piece and a metal plate, by changing the antenna form in the radar module equipment, the antenna main radiator passes through the clearance zone on the metal plate, so that the requirement of antenna radiation on the area of the clearance zone is reduced to the greatest extent. And meanwhile, electromagnetic coupling is generated through the coupling metal column and the metal plate of the product, so that the influence of insufficient clearance area of the metal plate on radar radiation performance is reduced. According to the utility model, the requirement of double antennas for the clearance area is reduced, the influence of insufficient clearance of the metal plate on the radiation performance of the radar is reduced, the requirement for the clearance area is minimized, the efficient performance of the radar is ensured, and the antenna has wide application prospect and commercial value.

A waveguide device. The waveguide device is mountable on a printed circuit board (PCB) and / or an integrated circuit device (MMIC), and includes: at least one bottom-side carrier, on which a first waveguide layer is arranged or which itself is designed as a first waveguide layer; a plurality of ball contact points, which are arranged in a predetermined pattern on the bottom-side carrier and at least partially surround a first lateral inner region of the first waveguide layer; at least a first top-side carrier, which is arranged in parallel with the bottom-side carrier and at least in regions opposite the first waveguide layer and at least in regions on the ball contact points, wherein at least one emission / incident aperture for radar radiation is present in the first top-side carrier and opposite the first lateral inner region.