162 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 discloses a radar radiation source model identification method based on multivariable time sequence Transform, and relates to radar radiation source identification. Comprising the following steps: 1) constructing a self-supervised contrast learning architecture by using PDW and IF data of the same radar signal, zooming in common features of the PDW and IF data, zooming out common features and specific features of the same domain, and performing cross-domain translation before inputting a model to ensure that a domain missing problem can be handled; 2) constructing a multivariable time sequence Transform model for processing the multivariable time sequence data, not modifying any structure of the Transform, and only redefining the function of each module, that is, learning the relationship between variables through an attention mechanism, and learning the long-time relationship through layer normalization and a feedforward network; data of different domains of radar signals are fully utilized, the degree of dependence of the model on the data is reduced, and the robustness of the model is improved; and the model identification effect of the model on the non-cooperative radar radiation source with limited data is improved.

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 relates to the technical field of communication, and discloses a radar radiation source identification method, which comprises the following steps: acquiring an original radiation source signal, and carrying out signal processing operation on the original radiation source to obtain a clear radiation source signal; performing vector quantization operation according to the clear radiation source signal to obtain a quantized radiation source signal; inputting the quantized radiation source signal into a pre-trained signal screening model to obtain a to-be-identified radiation source signal; according to the to-be-identified radiation source signal, performing a nearest neighbor node extraction operation to obtain a nearest neighbor node set; according to the nearest neighbor node set and a preset target node, performing calculation to obtain a signal distance; and according to the signal distance, carrying out maximum value extraction operation to obtain a maximum signal distance, and determining the radiation source corresponding to the maximum signal distance as a final radiation source. The method can improve the recognition precision of radar radiation source signals in a complex electronic countermeasure environment.

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 discloses a radar radiation source simulator based on a bidirectional microwave link, belongs to the technical field of radar target simulation, and solves the problem of high structural complexity of an existing radar radiation source simulator. The simulator comprises an antenna, a radio frequency transceiving assembly, a radar simulation assembly and a power amplifier, the antenna transmits a received radio frequency input signal to the radio frequency transceiving assembly; the radio frequency transmit-receive assembly performs down-conversion regulation and control on a radio frequency input signal to obtain an intermediate frequency output signal and a radio frequency detection result; the radar simulation assembly generates a multi-target radar simulation intermediate frequency signal according to the intermediate frequency output signal and the radio frequency detection result; the radio frequency transceiving assembly performs up-conversion regulation and control on the multi-target radar simulation intermediate frequency signal to obtain a radar simulation radio frequency output signal; the power amplifier performs power amplification on the radar simulation radio frequency output signal and then transmits the signal through the antenna; and a bidirectional microwave link is arranged in the radio frequency transceiving component and is used for realizing time-sharing regulation and control between up-conversion regulation and control and down-conversion regulation and control.

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 specifically relates to a radar signal sorting method based on 3D semantic segmentation Transformer, and the method comprises the steps: obtaining an original radar pulse sequence, and mapping the original radar pulse sequence into PDW point cloud data which comprises the four features of arrival time, carrier frequency, pulse width and amplitude; performing preprocessing on the PDW point cloud data; inputting the preprocessed PDW point cloud data into the trained 3D semantic segmentation Transform network for point cloud classification, and outputting the PDW point cloud data with label information; carrying out reverse normalization processing on the PDW point cloud data with the label information, and recovering the PDW point cloud data into original PDW point cloud data; and in the original PDW point cloud data with the tag obtained after reverse normalization, classifying the PDW data of the same type into radar signal pulse data of the same type according to tag information. The Transform network model provided by the invention can be used for mining the set relation of radiation source signals in a three-dimensional space, and has relatively high sorting capacity for a complex radar radiation source overlapping scene and the sorting problem of unknown signals.

The invention relates to a cloud detection method based on combination of a laser radar and a microwave radar, and belongs to the field of radio. The detection method comprises the steps that a coordinate system where a laser radar radiation antenna is located is used as a reference, and system pointing angle deviation caused by the installation position of a microwave radar extension set is corrected in the mode that angle tracking parameters are calculated according to laser-microwave radar extension set antenna installation differences, and scanning space angle synchronization is guaranteed; time synchronization is guaranteed by controlling the time of a radar terminal issuing a control signal to a laser radar extension set and a microwave radar extension set, so that radar beams arrive at meteorological particle echo information to be consistent; after the radar terminal collects data, characteristic parameter extraction and data fusion are carried out on the composite observation data of the laser radar and the cloud radar. According to the method, the problem of target angle deviation caused by spatial distribution difference of the laser radar and the microwave radar during composite observation of the laser-microwave radar system is solved, and the consistency of observed targets is improved.

The invention provides a detection scene simulation system based on radar signal sorting and positioning, which belongs to the technical field of radar detection and comprises a signal interception module, a pulse description word generation module, a signal sorting module and a direction-finding cross positioning module. In a radar detection process, a signal interception module is started, a signal from a radar radiation source is received, whether an interception condition is met or not is judged, and a waveform and a target signal are recorded if the interception condition is met. Then, the pulse description word generation module processes the intercepted signals to generate accurate pulse description words and time-frequency domain parameters; and then the signal sorting module further screens and classifies the signals by using the set sorting parameters and a signal identification sorting algorithm. And then, the direction-finding cross positioning module performs association and positioning calculation based on the sorting result. According to the method, the target signal can be quickly identified in a complex electromagnetic environment, the influence of interference signals is reduced, and the signal analysis capability of a radar detection system is enhanced.

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.

A method for monitoring the traffic flow with the aid of a stationary radar sensor, which operates in the frequency band of 76 GHz to 77 GHz and is operated in such a way that it meets a radiation protection criterion according to which, in a sequence of consecutive time intervals which all have the same length T, each of these time intervals includes an uninterrupted radiation-free time period of at least 0.9 T. An arbitrary stationary point in the surroundings of the radar sensor within each of these time intervals is exposed to the radar radiation of this radar sensor for no more than a radiation duration of L≤T / 100. The radar sensor is static during the measuring operation, and the radiation protection criterion is met by the temporally clocked operation of the radar sensor.

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 present invention discloses a method for positioning a spaceborne radar radiation source considering ionospheric correction. An observation equation of a reference station considering only the ionospheric influence is established. The double-difference equation is obtained by pairwise subtraction of the reference station observation equations, thereby eliminating the receiver clock error of the reference station and the receiver clock error of the navigation spaceborne transceiver, and obtaining the double-difference ionospheric delay corresponding to the corresponding sub-reference station. The double-difference ionospheric delays of each sub-reference station are combined with the approximate coordinates of the virtual observation station to obtain the double-difference ionospheric delays of each sub-navigation spaceborne transceiver corresponding to the virtual observation station. Then, short baseline solution is carried out in combination with the coordinates of the radiation source target without ionospheric correction to complete differential positioning and obtain the coordinates of the radiation source target after eliminating the ionospheric delay. The present invention uses the VRS method model to eliminate the influence of the ionosphere, and the positioning result has higher accuracy.

The invention relates to a method for operating a radar sensor device (2) for a motor vehicle (1), comprising the steps of providing a first radar sensor unit (6) of the radar sensor device (2) having a first detection range (29); providing a second radar sensor unit (7) of the radar sensor device (2) having a second detection range, different from the first detection range (29); arranging first sensor elements (25) of the first radar sensor unit (6) alternately with second sensor elements (26) of the second radar sensor unit (7) on a component (28) of the motor vehicle (1), at least in certain regions; and, emitting first radar radiation (4) by means of the first sensor elements (25) and simultaneously emitting second radar radiation by means of the second sensor elements (26). The invention also relates to a radar sensor device (2) and to a motor vehicle (1).

The present invention discloses a moving platform radar radiation characteristic measurement device, comprising: a drone subsystem for providing a synchronization signal and power supply for a receiving front-end subsystem and a detection and recording subsystem, flying according to a specified route, and recording the flight state; a receiving front-end subsystem for receiving in real time radar radiation signals in space, filtering them and then transmitting them to the detection and recording subsystem; a detection and recording subsystem for performing peak detection and sampling on the radar radiation signals and recording the corresponding data; and a data processing subsystem for performing validity screening and error compensation on the recorded data, and combining with the recorded data of the radar device itself to give an analysis result of the radar radiation characteristics. The present invention mainly uses a drone to load an electromagnetic radiation detection device to form an effective moving platform radar radiation characteristic measurement device, which has the characteristics of low hardware cost, fast equipment layout, wide applicable scenarios, etc.

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 radiation characteristics of a phased array radar are the spatio-temporal distribution characteristics of the radar transmission power, which are related to the combat and technical capabilities of the radar to detect targets. Traditional measurement methods are mainly used for static tests. When measuring the radiation characteristics of a phased array radar on a moving platform, due to the characteristics of its array surface system and beam arrangement, it is often necessary to customize the measurement method in combination with the specific detection scenario and environmental characteristics, and carry out measurement path planning. The above traditional measurement methods are no longer applicable. The present invention proposes a method for measuring route planning of the radiation characteristics of a phased array radar on a moving platform. Aiming at the transmission characteristics during the detection of the phased array radar, an unmanned aerial vehicle (UAV) is used to carry an electromagnetic radiation receiving device. According to the expected movement path of the radar under test in the test scenario, the prior spatio-temporal distribution of the radar radiation characteristics is obtained. Under the constraints of the flight boundary conditions of the UAV and the boundary conditions of the electromagnetic radiation receiving device, the measurement positions and measurement duration of the radiation characteristics are determined, and the UAV flight route is designed to cover all measurement positions and meet the measurement duration.

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.