33 results about "Emitter identification" patented technology

A system and method are described for emitter identification and tracking in an electronic warfare (EW) environment. The system includes an antenna array configured to receive signals from radio frequency (RF) emitters during a dwell. Processing circuitry converts the received signals into digital signals. Pulses are detected and characteristics of the pulses determined to form pulse descriptor words (PDWs). The PDWs obtained during the dwell are deinterleaved using unsupervised machine learning to form clusters. The clusters are categorized using one or more supervised machine learning algorithms to determine whether the PDWs correspond to known or unknown emitters and the results tracked as in or out of library emitters. After merging the in or out of library emitters, an emitter report is generated and used to update a library of emitter profiles used by the supervised machine learning algorithms as well as determine countermeasures to generate.

A system and method are described for updating Machine Learning (ML) models in an electronic warfare (EW) environment. The ML models are updated automatically post mission using threat data of emitters in the EW environment and then deployed to hardware in an aircraft. The updated ML models are used during a subsequent mission and include an unsupervised ML model to deinterleave waveforms received from the emitters and a supervised ML model for emitter identification, waveform tracking, and anomaly detection based on the deinterleaved waveforms. The ML models are updated by augmenting templates that indicate the behavior of the emitters and training the ML models using many plausible superpositions of the augmented templates. The ML models are updated by selecting and applying non-linear augmentations of at least one of the templates and new templates randomly using a Monte Carlo approach.

A system and method are described for emitter identification, emission tracking, and anomaly detection in an electronic warfare (EW) environment. Association results are obtained of waveforms of the emitters in a current dwell. The association results include a current distribution of inferred groupings of the waveforms. Features are generated for each waveform by comparing the current distribution and recent emitter historical behavior contained in a Dynamic Emitter Library (DEL). A probability of association with a track is determined for each waveform based on the features generated through the comparison. An identity of an emitter based on the probability and anomalous behavior of the emitter are inferred for each waveform.

The application relates to a kind of open set transmitting source identification method, it is related to wireless communication and signal processing technical field, solve the problem such as existing method under open set scene, it is difficult to accurately exclude unknown device, classification accuracy and robustness are insufficient etc..The method only retains Q component by cutting to fixed length IQ signal, and extracts the absolute value and spectrum amplitude of the Q component, and is fused into three-channel pseudo-image as neural network input.Classification branch is optimized for cross entropy for known training equipment, to realize closed set classification;Contrast branch simultaneously utilizes known and unknown training samples to construct positive samples, known negative samples and open set negative sample mask, reduces the similarity of unknown samples through dynamic suppression boundary mechanism and calculates contrast loss.Furthermore, according to the dynamic adjustment of classification loss weight, the convergence of known category and the dispersion of unknown samples are realized.

The present application relates to the field of radio signal identification, and particularly relates to a cross-domain few-shot specific emitter identification method based on self-supervised learning and transfer learning, comprising: adopting a self-supervised joint embedding method, pre-training a first model and a second model by using an upstream task pre-training dataset, regularizing a first autocorrelation matrix output by the first model by using von Neumann entropy, calculating the cosine similarity between the feature projections output by the first model and the second model, and updating the parameters of the first model based on the regularized autocorrelation matrix and the cosine similarity; migrating the pre-trained first encoder to a feature extractor, inputting a downstream task fine-tuning dataset into the connected feature extractor and classification head to fine-tune the feature extractor and the classification head; inputting a wireless signal into the fine-tuned feature extractor and classification head to obtain the predicted transmitter category of the signal. The present application achieves good identification accuracy under the conditions of few samples and low cost.

The invention discloses a multi-head self-attention radiation source identification method based on fractional order Fourier transform driving. The method comprises the following steps: extracting multiple fractional domain features of a signal by using fractional order Fourier transform; a multi-head self-attention network based on a complex field is provided, fractional domain features are enhanced, reconstructed and compressed, and radio frequency fingerprint features with high identification degree are extracted; and the extracted features are classified by using a lightweight re-convolutional neural network. The method can be used for extracting the high-identification-degree fingerprint features of the radiation source in a complex electromagnetic environment, and is suitable for individual classification in a multi-radiation-source scene.

The invention discloses a frequency hopping radiation source identification method and system based on fragment polarization fingerprint fusion analysis, and belongs to the technical field of signal processing and target identification. In order to solve the problems that polarization fingerprints are fragmented due to multi-channel switching in frequency hopping communication and traditional radio frequency fingerprints are poor in recognition robustness under a low signal-to-noise ratio, a mechanism fusing multi-dimensional polarization features and a deep learning model is provided. The method comprises the following steps: collecting a frequency hopping signal polarization fingerprint, and converting complex polarization data into a one-dimensional dual-channel sample suitable for convolutional neural network input; constructing a CNN model with an adaptive rectangular convolution kernel and a channel attention mechanism, and training the CNN model to classify polarized fingerprint fragments of each frequency band; and a soft voting mechanism is adopted to carry out weighted fusion on the results, the fusion probability is compared with a threshold value, and an identification result is output or artificial discrimination is carried out. The method can effectively extract and fuse the fragmented polarized fingerprint features, and achieves the high-precision and high-robustness recognition of an unknown radiation source in a frequency hopping environment.

The present disclosure relates to a method for detecting a skimming device in a point of sale, POS, the method comprising: a plurality of successive scanning cycles each comprising scanning for emitters using a wireless communication protocol to receive emitter identifiers; generating and updating a list of detected emitters comprising for each currently detected emitter an emitter identifier and a first detection time; for each of the currently detected emitters in the detected emitter list, comparing an active time threshold with an active time of the currently detected emitter from the first detection time of the currently detected emitter, and / or comparing the identifier of the currently detected emitter with identifiers in a whitelist of authorized identifiers; and when a currently detected emitter has an active time exceeding the active time threshold and an identifier not belonging to the whitelist, activating an alert signal.

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.

A method for time synchronization of sensors in a distributed system, wherein an up-to-date value of an internal time unit of a sensor is stored when capturing sensor data, at least the value of the internal time unit stored when a characteristic time feature of an identification signal is received, an associated transmitter identification of the identification signal and a further value of the internal time unit is transferred together with the sensor data to the central unit, and where the central unit captures voltage and phase values from at least one phase conductor of a power grid as the current pointer over more than one measuring period, and the current pointers of the respective sensor unit are aggregated to form aggregated current pointers that are transferred to the central unit, and correct-phase powers are calculated from the voltage values and the aggregated current pointers in the central unit.

The present application relates to the technical field of radiation source identification, and particularly relates to a specific radiation source identification method and system based on multi-sequence feature learning, which generates simulated signals of I / Q path in-phase and quadrature signals of multiple modulation types according to the mechanism of radiation source fingerprint characteristics, and uses the simulated signals of the radiation source to build a sample data set; the signals in the sample data set are standardized to obtain multi-sequence signals; a sequence fusion convolution network model is constructed and trained and optimized using the multi-sequence signals in the sample data set; the radiation source signal to be identified is input into the trained and optimized sequence fusion convolution network model, and the modulation category of the radiation source signal to be identified is obtained using the trained and optimized sequence fusion convolution network model. The present application can improve the efficiency of radiation source identification, can identify multiple types of illegal entry radio stations, can ensure the security of communication networks, and can provide a basis for counter-decision in the field of counter-reconnaissance.

The application discloses an ADS-B radiation source identification method based on a convolutional neural network and ensemble learning, and comprises the following steps: acquiring an actual signal received by an ADS-B, and truncating actual ADS-B data according to the data characteristics of a synchronization header, a DF bit, a CA bit, an ICAO address code, a ME field message bit and a parity check bit; establishing two neural network structures; adopting a non-end-to-end and end-to-end processing mode for different data bits, inputting into the two neural network structures for training respectively, and extracting classification features from multiple dimensions; and utilizing multiple deep learning devices, and adopting a stacking method to fuse neural network feature results. Through the fusion of neural network feature results, the collective ability advantage of the individual learning device is obtained by adjusting the weight value, the obtained classifier has the advantages of high identification accuracy and strong generalization ability.

The invention belongs to the technical field of microphones, and relates to a transmitter identification prompting method applied to a wireless microphone system and related equipment, and the method comprises the steps that a receiver receives a transmitter identification request carrying a target transmitter identifier; the receiver generates a digital control instruction according to the target transmitter identifier, and the digital control instruction comprises the target transmitter identifier and an action instruction; the receiver sends the digital control instruction to the transmitter; the transmitter receives the digital control instruction and performs identifier analysis operation on the digital control instruction to obtain a target transmitter identifier; the emitter carries out identification matching operation on the target emitter identification according to the identification information of the emitter to obtain a matching result; if the matching result is that the digital control instruction is matched, the transmitter carries out instruction analysis operation on the digital control instruction to obtain an action instruction, and the action instruction is executed; if the matching result is not matched, the emitter executes an ending operation; according to the invention, target emitter identification and instruction execution can be completed rapidly and accurately.

A system and method are described for updating Machine Learning (ML) models in an electronic warfare (EW) environment. The ML models are updated automatically post mission using threat data of emitters in the EW environment and then deployed to hardware in an aircraft. The updated ML models are used during a subsequent mission and include an unsupervised ML model to deinterleave waveforms received from the emitters and a supervised ML model for emitter identification, waveform tracking, and anomaly detection based on the deinterleaved waveforms. The ML models are updated by augmenting templates that indicate the behavior of the emitters and training the ML models using many plausible superpositions of the augmented templates. The ML models are updated by selecting and applying non-linear augmentations of at least one of the templates and new templates randomly using a Monte Carlo approach.

The invention discloses an open set specific radiation source identification method and system based on multi-granularity embedding guidance. The method comprises the following steps: firstly, respectively extracting local fine granularity features and global time sequence dependence features of radio frequency signals by using a selective attention complex convolution branch and a Transform branch; carrying out cross-granularity alignment and semantic fusion on the dual-path features through an attention-guided multi-scale feature fusion module; constructing a label perception discrimination prototype embedding space, and optimizing the embedding space by using joint loss to enhance intra-class aggregation and inter-class separability; and projecting the rejected unknown class sample to a discriminative embedding space, and realizing further identification and subdivision of the unknown class by using an angle-based prototype increment updating mechanism. According to the invention, in an open set scene, high-precision identification of a known radiation source and detection and subdivision of an unknown radiation source can be realized at the same time, and generalization ability and identification stability in a complex real environment are significantly improved.

The application provides a model embedding-based radiation source identification method, different modal data are jointly input, an end-to-end multi-modal identification framework is trained, parameter sharing between different modal models is realized, compared with multiple modal corresponding to multiple network identifications, the network scale is obviously reduced, the convergence speed is improved, and in the process of sharing parameters of multiple modes, the network obtains more common information between modes. Meanwhile, after the end-to-end identification framework, a fusion algorithm based on the Bayes theory is provided, compared with using a single mode as the network output, since the output after fusion comes from different modes, the confidence of the result is higher, therefore, the fused output is used as the decision of the radiation source individual identification, the iteration number required during network convergence is reduced, and the effect of optimizing the network performance is achieved.

A method and system are disclosed for identifying an emitter or transmitter (e.g., among a plurality of emitters) to a desired degree of accuracy. The method or system may receive a signal in a receiver from a transmitter, wherein the signal propagates from the transmitter to the receiver. The method or system may demodulate the received signal to generate a demodulated signal having a carrier frequency of zero and determine a bispectrum associated with the demodulated signal. The method or system may classify the bispectrum into a classification using a neural network having been trained to a signal emitted by the transmitter. The method or system may determine an accuracy of the classification based on a probability of the classification being greater than a threshold.

Methods, systems, and devices for wireless communication are described. Various aspects relate generally to continuous wave (CW) emitter identification in ambient internet of things (A-IoT) systems or deployments. Some aspects more specifically relate to signaling mechanisms according to which a first wireless device may indicate, to a second wireless device, whether the first wireless device is a CW emitter device. A CW emitter device may be a device that is at least capable of transmitting a CW signal. The first wireless device may indicate whether the first wireless device is a CW emitter device in various ways. In some aspects, the signaling mechanisms that the first wireless device and the second wireless device use to communicate information indicative of whether the first wireless device is a CW emitter device may depend on a capability or mode of the first wireless device.

The application discloses a radar radiation source identification system based on a time domain graph tensor attention network, realizes the connection between different sampling points and different radar features, and thus accurately identifies the radar radiation source. The identification system is composed of a radar acquisition module, a database and an upper computer. The radar acquisition module acquires the radar radiation source signal and stores the radar radiation source signal into the database. The upper computer collects the data in the database, performs signal transformation on the collected data, models a robust attention identification model based on the time domain graph tensor data after signal transformation, and detects new radar radiation source signals by using the robust attention identification model. The application realizes online identification of the radar radiation source identification with strong intelligence, high precision and high accuracy, and solves the problems of low identification precision of the radar radiation source and the fact that the time domain correlation and feature correlation are not considered.

Method and system for time synchronization of sensor units. Method for time synchronization of sensor units in a distributed system, wherein sensor data is acquired by the sensor units and transmitted to a central unit, wherein, upon acquisition of the sensor data, a further current value of an internal time unit of the sensor unit is stored, wherein the sensor data is at least assigned the value of the internal time unit stored upon receipt of a characteristic signal, the transmitter identification of the characteristic signal and the further value of the internal time unit stored upon acquisition of the sensor data and transmitted to the central unit together with the sensor data, wherein the central unit acquires voltage and phase values of at least one phase conductor as current vectors over more than one measurement cycle and aggregates the current vectors to an aggregated current vector and transmits the aggregated current vector to the central unit and calculates the power from the voltage values and the aggregated current vector.

The invention belongs to the technical field of microphones, and relates to a transmitter identification prompting method applied to a wireless microphone system and related equipment, and the method comprises the steps that a receiver receives a transmitter identification request carrying a target transmitter identifier; the receiver generates a digital control instruction according to the target transmitter identifier, and the digital control instruction comprises the target transmitter identifier and an indicating lamp output instruction; the receiver broadcasts the digital control instruction according to the wireless audio communication link; when the transmitter receives a digital control instruction, performing identification analysis operation on the digital control instruction to obtain a target transmitter identifier; the emitter carries out first identifier matching operation on the target emitter identifier according to the identifier information of the emitter; if the first matching result is successful matching, the transmitter performs control instruction analysis operation on the digital control instruction to obtain an indicating lamp output instruction, and executes the indicating lamp output instruction; according to the invention, the target emitter can be accurately and rapidly identified.

The embodiment of the invention discloses a radar radiation source individual identification method and device for airport low-altitude security and protection. A specific embodiment of the method comprises the following steps: generating a radar signal fragment group set; performing self-supervised pre-training on the radiation source feature extraction network to update model parameters; constructing a radar radiation source individual identification model; performing joint optimization on the radar radiation source individual identification model through the known radiation source signal group set to generate a known radiation source center feature set; generating a known radiation source boundary parameter set according to the radar radiation source individual identification model and the known radiation source center feature set; and according to the known radiation source boundary parameter set and the radar radiation source individual identification model, performing radiation source individual identification on the real-time radar signal to generate a radar radiation source identification result. According to the embodiment, the generalization ability of the recognition model can be improved under the small sample condition, so that the individual recognition accuracy of the radiation source is improved, and the low-altitude security and protection safety of an airport is improved.

The embodiment of the invention belongs to the technical field of microphones, and relates to a transmitter identification prompting method applied to a wireless microphone system and related equipment, and the method comprises the steps that a receiver receives a transmitter identification request carrying a target transmitter identifier; the receiver generates a digital control instruction according to the target transmitter identifier, and the digital control instruction comprises the target transmitter identifier and an action instruction; the receiver sends the digital control instruction to the transmitter; the transmitter receives the digital control instruction and performs identifier analysis operation on the digital control instruction to obtain a target transmitter identifier; the emitter carries out identification matching operation on the target emitter identification according to the identification information of the emitter to obtain a matching result; if the matching result is that the digital control instruction is matched, the emitter performs action analysis operation on the digital control instruction to obtain an action instruction, and the action instruction is executed; according to the invention, identification and instruction execution of the target emitter can be completed rapidly and accurately.

This invention proposes a class-incremental radar radiation source identification method based on temporal adaptive clustering and orthogonal perturbation pseudo-feature generation. The steps are as follows: The acquired radar radiation source signal is constructed in three channels, including the time-domain waveform, the real part and imaginary part of the Fast Fourier Transform, and then standardized; fused features are extracted using multi-scale convolution and a multi-head self-attention network; the feature extractor is frozen, and the old class features are subjected to temporal adaptive clustering according to category, dynamically determining the number of clusters based on temporal complexity; pseudo-feature sets are generated based on the cluster centers through intra-class Gaussian perturbation and class boundary orthogonal perturbation; the old class pseudo-features are merged with the new class real data, and multi-stage incremental training is used to achieve rapid adaptation of the new class and maintain the performance of the old class; features are extracted and classified and output during the inference stage using the same steps. This invention maintains high recognition accuracy even without old class real data and is suitable for continuous identification of individual radars in dynamic electromagnetic environments.

The present application belongs to the technical field of radar emitter identification, and particularly relates to a radar emitter individual open set identification method and system, a signal identification model is constructed, and the signal identification model is trained by using labeled signal samples in a radar signal database, wherein a transient sequence of the radar signal is used as network input of the signal identification model, signal features of the input are extracted by the signal identification model network, and the extracted signal features are classified and identified; the receiver intercepts a radar emitter individual signal to be identified as signal identification model input, the trained signal identification model is used to identify the radar emitter individual signal to be identified, if the identification is a known class, a name of the radar emitter of the known class is output, if the identification is an unknown class, the signal is labeled as the unknown class, and is added to the radar signal database for identification and labeling by manual operation. The present application has good generalization and fast identification speed, and has the abilities of classifying known emitters and detecting new unknown emitters, and is convenient for practical scene application.

A system and method are described for emitter identification, emission tracking, and anomaly detection in an electronic warfare (EW) environment. Association results are obtained of waveforms of the emitters in a current dwell. The association results include a current distribution of inferred groupings of the waveforms. Features are generated for each waveform by comparing the current distribution and recent emitter historical behavior contained in a Dynamic Emitter Library (DEL). A probability of association with a track is determined for each waveform based on the features generated through the comparison. An identity of an emitter based on the probability and anomalous behavior of the emitter are inferred for each waveform.

The invention discloses a radar communication radiation source identification method and system based on multi-modal feature alignment, and belongs to the technical field of electronic reconnaissance and signal processing. The method comprises the following steps: preprocessing a received radar signal to generate a standardized time-frequency graph; extracting a signal feature vector through a specially designed convolutional neural network encoder; extracting a text feature vector by using a Transform encoder; through joint optimization of cosine similarity loss and physical parameter constraint loss, alignment of signal-text features in a unified vector space is realized; and finally, zero sample identification of unknown radar signals is realized through vector similarity calculation. According to the method, the problems of low recognition rate and insufficient cross-modal information fusion in a low signal-to-noise ratio environment of a traditional method are effectively solved, and the recognition precision and the system robustness are remarkably improved.

The application discloses a radar radiation source identification method based on DCNN and Transform, introduces the Transform architecture into the field of radar radiation source identification, improves the architecture, overcomes the previous learning long-range dependency problem, and breaks the previous convolutional neural network limitation by calculating the correlation between two positions. An attention mechanism model is adopted to extract the data features of multiple channels, learn the data channel features before global feature extraction, learn the channel dependency, highlight the importance of each channel after feature extraction, and improve the representation ability of the radiation source time-frequency diagram. The method provided by the application can firstly perform local feature extraction on the time-frequency diagram with low signal-to-noise ratio, then consider the relationship between the correlation positions and the global features, and comprehensively consider the local features and the global features under the condition of signal-to-noise ratio, so that good identification effect can be achieved.

The invention relates to a cross-domain incremental radiation source identification method. Comprising the steps of generating an original training set, inputting the original training set into a radiation source feature extraction network model, and obtaining a multi-scale feature tensor; obtaining a final model parameter based on the multi-scale feature tensor; all model parameters except the classifier in the final model parameters are frozen, the classifier is updated, and an incremental radiation source feature extraction network model is obtained; collecting a radio frequency signal of a newly added radiation source to obtain incremental radio frequency time domain data, and obtaining an incremental feature set by using the frozen model parameters except the classifier; and generating an incremental training set and training a classifier to obtain an optimal incremental radiation source feature extraction network model, and identifying original and newly registered incremental radiation sources based on the network model. The method has strong adaptability to distribution offset of signal features in different environments, can perform online incremental expansion on newly appearing radiation source feature categories, and has the advantages of light model and low calculation and storage overhead.

In order to solve the core problems that an existing SEI method is good in performance under idealized fixed parameters, but recognition performance is reduced due to working parameter changes (especially zero sample parameters) in an actual environment, and RFF features are weak and prone to being interfered by IM information, the invention provides a method for improving the recognition performance of the SEI method. The invention provides a zero-sample specific radiation source identification method and system based on metric decoupling learning, and belongs to the technical field of specific radiation source identification. According to the method, the measurement decoupling representation model is designed, the interference influence of IM information on RFF feature representation is weakened, and the measurement decoupling loss function is designed for the model, so that the causal logic relationship between the RFF and IM information in the RF signal is mined, and the independent RFF features are represented. Meanwhile, through an accumulation measurement recognition method, the model can deduce the radiation source identity of the zero sample working parameters, and the generalization performance of the model under the condition of the zero sample working parameters is ensured. The method is suitable for specific radiation source identification in a complex electromagnetic environment.