216 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 radiation source individual identification method and system based on unintentional phase modulation characteristics, and relates to the technical field of radar signal processing. The method comprises the following steps: acquiring intermediate frequency data of radar signals emitted by different radar radiation source individuals to obtain a plurality of intermediate frequency data, and extracting a plurality of monopulse signals corresponding to the plurality of intermediate frequency data from the plurality of intermediate frequency data; phase extraction is carried out on the multiple monopulse signals, and unintentional modulation phase feature maps corresponding to the multiple pieces of intermediate frequency data are obtained; and constructing a radar individual identification model and training by using the data set, and identifying the plurality of unintentional modulation phase feature maps through the trained radar individual identification model to obtain a plurality of radar radiation source individual identification results. By extracting unintentional modulation phase features of radar signals, original fingerprint information of the signals is reserved to reduce calculation complexity, and a deep residual network of a small convolution kernel is constructed as a radar individual recognition model to improve recognition accuracy.

The invention discloses a radar radiation source individual identification method based on multi-domain feature joint judgment. The technical scheme comprises the following steps: step 1, signal preprocessing; 2, original signal detection and parameter measurement; 3, signal feature extraction and multi-feature-domain conjoint analysis are carried out; and step 4, signal characteristic quantity visualized visual display is carried out. Therefore, multi-domain feature extraction and recognition of the radar radiation source individuals are realized. According to the method, the multi-domain features of the radar radiation source signals are extracted, multiple kinds of reference information are provided for distinguishing a radar carrier platform, the problem that the single feature recognition effect is poor is solved, and the radar radiation source individual recognition accuracy is improved; a set of powerful tool is provided for a user by utilizing an MATLAB GUI user interface prototype, so that radar signals can be better understood and analyzed. A user can realize comprehensive analysis from an original signal to multi-dimensional fine characteristics through the interface, so that the user can deeply research signal characteristics, and more accurate decision making and further research are facilitated.

The invention discloses a dynamic threat-based LGVF-APF-SAC spacecraft path planning method, a storage medium and an electronic device, and the method specifically comprises the following steps: firstly, building a real-time risk function according to a dynamic threat (radar detection exposure risk), and further generating an early warning region according to the risk function (radar radiation); based on the early warning area, an improved dynamic time-varying LGVF is used to construct a guide potential field based on the dynamic time-varying LGVF, so that the spacecraft is guided to avoid the early warning area to reach a target point. Then, an end-to-end reinforcement learning framework is developed; according to the framework, a maximum entropy RL (SAC) algorithm is selected, and a constructed guide potential field is integrated into a learning process in a reward function form so as to evaluate and optimize decision actions of a spacecraft. According to the algorithm provided by the invention, the dynamic adaptive capacity and reliability of path planning can be improved while the dynamic overload condition of the spacecraft is ensured to be met.

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 signal sorting method based on a CNN-LSTM composite depth model, and the method comprises the steps: carrying out the simulation generation of aliasing radar radiation source pulse signal data, building a CNN network model, adding an LSTM layer, obtaining a CNN-LSTM composite depth model, carrying out the preprocessing of the aliasing radar radiation source pulse signal data, dividing a training set and a test set, and carrying out the recognition of the aliasing radar radiation source pulse signal data. And training the CNN-LSTM composite depth model by using the training set, finally inputting test set data to the trained CNN-LSTM composite depth model, and carrying out aliasing radar radiation source pulse signal sorting to obtain a radar radiation source sorting result. Compared with an existing method and a sorting method based on a full-connection network, the method has the advantages that the sorting accuracy is higher, the robustness is high under the condition that the leakage pulse rate and the false pulse rate in input pulses are high, a new theoretical support and a technical approach can be provided for radar radiation source signal sorting in a modern complex electromagnetic environment, and the method is suitable for popularization and application. And effective help is provided for radar signal receiving and analysis.

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 radar radiation type system simulation, and particularly discloses a radio frequency simulation dynamic game anti-interference environment simulation system and method. According to the interference environment simulation system architecture and the interference environment simulation method, environment signal detection is achieved, real-time dynamic updating of interference performance can be achieved according to detection results and confrontation strategy analysis and adjustment, dynamic game confrontation environment simulation can be achieved in radio frequency simulation, real battlefield interference environment signals can be simulated more vividly, and the interference performance of a battlefield is improved. The method provides support for performance evaluation of radar equipment in a complex electromagnetic environment, and has good popularization and application values.

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 discloses a radar radiation source individual identification method based on an incremental neural network, and the method comprises the steps: firstly obtaining a radar radiation source data sample, and extracting the time-frequency features and bispectrum features of the radar radiation source data sample; secondly, on the basis of the ResNet1D network, a radar radiation source individual recognition network model is constructed in combination with efficient channel attention ECA and a self-organizing incremental learning neural network SOINN; and finally, combining the time-frequency features and the bispectrum features into a dual-channel sample, training and testing through a radar radiation source individual identification network model, outputting a radar radiation source individual identification result, and completing radar radiation source individual identification. The method achieves the screening of useful features, improves the learning capability of the features, improves the recognition precision, and completes the precise recognition of a radar radiation source individual.

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.

The invention belongs to the technical field of radar target identification and radiation source identification, and relates to a compressed residual network and a radar radiation source signal identification method. The method comprises the following steps: preprocessing a radar radiation source signal to obtain a preprocessed image; the preprocessing comprises CWD time-frequency transformation, graying and time-frequency image size adjustment; the preprocessed image is sent to a conv1, then maxpooling processing is carried out, and then the image passes through a conv2; and after the images sequentially pass through conv3 to conv6 and GAP, inputting the images processed by the GAP into softmax for classification, and outputting a classification result. Each of the conv2 to the conv6 uses a residual unit; and when the sizes and dimensions of the input and output feature maps are inconsistent, matching the input feature maps by adopting a 1 * 1 convolution kernel. The method has a good identification effect on the large-similarity radar radiation source.

The invention provides a radiation source individual identification method and system based on continuous pulses, and relates to the technical field of radar signal processing. The method comprises the following steps: acquiring intermediate frequency data of radar signals emitted by different radar radiation source individuals to obtain a plurality of intermediate frequency data, and respectively extracting a plurality of continuous pulse signals of the plurality of intermediate frequency data from the plurality of intermediate frequency data; fuzzy function features of the multiple continuous pulse signals are obtained through a fuzzy function and fused, and fuzzy function feature maps of the multiple pieces of intermediate frequency data are obtained; and after training the constructed radar individual identification model, identifying the plurality of fuzzy function feature maps to generate a plurality of radar radiation source individual identification results, and updating and replacing the radar individual identification model based on the identification results. By extracting the features of a plurality of continuous pulses, the signal time sequence information can be fully utilized to reduce noise interference, and the model is updated in time to improve the recognition rate and generalization ability of the model.

The present invention relates to a radome (10) which is configured to cover a vehicle radar, comprising: a first plastic component (1); a second plastic component (2); at least one interface (4) formed between the first plastic component (1) and the second plastic component (1), wherein the first plastic component (1) is connected to the second plastic component (2) at the interface (4) by injection molding; a radar area (7) which is configured for the transmission of radar radiation from the vehicle radar, wherein the at least one interface (4) is arranged at least partially in the radar area (7).

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.