236 results about "Radar interference" patented technology

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation inversion method for lifting rail offset correction, and mainly relates to the technical field of surface deformation monitoring. In order to solve the problem that the deformation monitoring quantity of the existing multi-dimensional small baseline set formed aperture radar interference is obviously lower than the actual deformation quantity, a time sequence deformation observation equation is established by using multi-orbit synthetic aperture radar data, an ascending-orbit small baseline set InSAR settlement monitoring point cloud and a descending-orbit small baseline set InSAR settlement monitoring point cloud are calculated respectively, point cloud registration is carried out, and the settlement monitoring point cloud of the ascending-orbit small baseline set InSAR settlement monitoring point cloud is calculated to obtain the settlement monitoring point cloud of the descending-orbit small baseline set InSAR settlement monitoring point cloud. Calculating the offset of the monitoring result of the lifting rail in the vertical, east-west and south-north directions; calculating to obtain an ascending orbit incidence angle correction number and a descending orbit incidence angle correction number of the synthetic aperture radar image by using the offsets of the ascending orbit monitoring result in the vertical direction, the east-west direction and the south-north direction; and finally, constructing a multi-dimensional small baseline set InSAR deformation inversion model containing lifting rail offset correction, solving to obtain a corrected deformation monitoring quantity, and improving the deformation monitoring precision.

The invention discloses a radar interference identification method based on multi-representation domain feature interactive fusion and a graph structure semantic classifier, and the method comprises the steps: constructing a multi-representation domain feature interactive fusion module, taking a time domain waveform, a spectrogram and a time-frequency spectrogram as input data, and outputting fusion features; constructing a graph structure semantic classifier, inputting the fused features into the graph structure semantic classifier, analyzing semantic association of different types of signals by the graph structure semantic classifier, and outputting a classification result; dividing the data set into a training set and a test set, and storing network parameters with the best classification effect in the training set according to a classification result; and loading network parameters, and inputting the test set into the radar interference identification network for processing to obtain an identification result. According to the method, the interference signals can be fully represented, the identification and processing capability of the interference signals is improved, robustness representation is carried out on the interference signals, and the accuracy of radar interference identification is improved.

The invention provides a radar complex interference identification method based on a feature fusion network, and the method can automatically learn and extract useful features from original radar interference signals through employing an interference signal time domain and time-frequency domain feature extraction network, and can adapt to more complex signals. And fusing all the features, and sending the fused features to a trained feature classification fusion network for classification. A model formed by the interference signal time domain and time-frequency domain feature extraction network and the feature classification fusion network has strong generalization ability, and a complex mode can be learned from a large amount of data, so that unknown interference can be identified to a certain extent; after model training is completed, the reasoning (prediction) speed is relatively high, and real-time interference identification can be provided; in addition, the model can also process complex, nonlinear and hybrid interference signals.

The invention relates to a multifunctional radar interference strategy generation method and device, computer equipment and a storage medium, and the method comprises the steps: employing a part-observable Markov decision model, carrying out the modeling of a non-cooperative game process of multifunctional radar confrontation according to an OODA cycle, obtaining a multifunctional radar confrontation process model, and carrying out the calculation of the model. And an interference strategy generation network based on Transform and A2C is designed. A plurality of TIT units are used as observation feature extractors, action probability distribution is output through a strategy network, state value evaluation is carried out through a value network, and a strategy-value double-branch decoupling structure is formed. Under the constructed multifunctional radar adversarial process model, the interference strategy generation network is trained, the trained interference strategy generation network is utilized to generate the corresponding interference strategy, and the effective and accurate interference strategy can be generated in real time by adopting the method.

The invention discloses a cognitive radar active anti-interference waveform generation method and system based on DQN and MAB, and the method comprises the steps: obtaining a radar interference time-frequency diagram based on a radar transmitting signal and an interference signal; based on the radar interference time-frequency diagram, simulating a radar confrontation process and constructing a transmitted waveform strategy library; solving the waveform strategy library by using a DQN algorithm, and optimizing a solving result by using an MAB algorithm to obtain an optimal transmitted waveform; and a CFAR detector is adopted to detect the optimal emission waveform, and the optimal anti-interference waveform and parameters are generated. According to the method, the global strategy search of the DQN and the local parameter optimization of the MAB are fused, the anti-interference dynamic adaptation problem in a multi-pulse time sequence interaction scene is effectively solved, and the method is suitable for the real-time anti-interference requirements of an airborne radar electronic countermeasure system and a complex electromagnetic environment.

The embodiment of the invention relates to the technical field of radars, in particular to a radar interference signal semi-supervised recognition method, system and device and a medium, and the method comprises the steps: carrying out the short-time Fourier transform of a radar interference signal, and constructing a training sample set containing a labeled training sample and an unlabeled training sample; pre-training the CNN network based on the labeled training sample to obtain a pre-training model; using the pre-training model to predict the unlabeled training samples, generating pseudo labels, screening the pseudo labels, and using the retained unlabeled training samples with high confidence to calculate a semi-supervised loss value; performing category perception comparison learning on the pre-training model by using the unlabeled training sample, and calculating a category perception comparison loss value; calculating a total loss value based on the semi-supervised loss value and the category perception comparison loss value, and performing fine tuning on the pre-training model to obtain an identification model; and inputting the radar interference signal to be identified into the identification model to obtain an identification result output by the identification model.

A wireless transmit receive unit (WTRU) and methods are disclosed for mitigating radar interference on PUSCH transmission. The method may include: transmitting a message informing a network of PUSCH MIMO layer aggregation capability; receiving a MIMO rank; receiving an indication to use PUSCH MIMO layer aggregation; determining a PUSCH layer aggregation level based on the MIMO rank; and transmitting multiple redundancy versions of a PUSCH codeword comprising one or more code blocks based on the PUSCH layer aggregation level. The WTRU may be configured to transmit a message informing a network of PUSCH MIMO layer aggregation capability; receive a MIMO rank; receive an indication to use PUSCH MIMO layer aggregation; determine a PUSCH layer aggregation level based on the MIMO rank; and transmit multiple redundancy versions of a PUSCH codeword comprising one or more code blocks based on the PUSCH layer aggregation level.

The invention discloses a radar interference resource allocation method based on a grey wolf algorithm. The method mainly solves the problems that in the prior art, interference resource allocation efficiency is low, combination explosion occurs, and engineering implementation is not facilitated. The scheme comprises the following steps: 1) constructing an interference task scene, and constraining a solution space through a coding strategy optimization method; 2) constructing an interference resource allocation objective function, and initializing an allocation scheme according to a good point set theory; 3) on the basis of the grey wolf algorithm, a nonlinear convergence factor, a dynamic weight and a reverse learning method are introduced to complete updating of a grey wolf position, namely updating of a distribution scheme is completed; and 4) repeating the iterative updating process in the step 3) until the algorithm converges, and taking the wolf pack position corresponding to the currently obtained interference benefit as a final interference resource allocation result. The method can effectively improve the optimization speed in the resource allocation process, avoids falling into a local optimal solution, and can be used for improving the resource allocation task efficiency in a radar cooperative interference scene.

The invention discloses a dynamic modeling method of a hidden Markov model, which comprises the following steps of: defining a radar-jammer master-slave confrontation mode, namely, when a radar is not interfered, setting an HMM-based information scheduling strategy matrix A and a transmitted pulse time-frequency parameter control strategy matrix B in coherent processing time according to a target detection task of the radar; a transmission pulse sequence is generated. An interference party extracts radar state parameters by using intercepted radar pulses, estimates a radar information scheduling strategy matrix A and a transmitting pulse time-frequency parameter control strategy matrix B, formulates an interference strategy matrix Aj and an interference signal time-frequency parameter control strategy matrix Bj, sends interference signals to the radar, and estimates Aj and Bj of the interference party after the radar receives the interference signals. And judging whether interference is valid or not, correspondingly adjusting the matrix A and the matrix B, and repeating the process to realize dynamic modeling of cognitive radar countermeasure.

The invention discloses a networking multifunctional radar interference decision-making method based on threat level sorting. The method comprises the following steps: A, determining the working mode of each radar of a networking multifunctional radar; b, performing threat level sorting on the radar according to the working mode; c, inputting the interference pattern mask matrix and the one-hot code of the radar with the highest threat level into an interference decision network according to the radar sorting, so that the network outputs an interference pattern; d, updating the interference pattern mask matrix according to the interference pattern selected at the previous moment, and performing interference decision on the radar of the next threat level; repeating the step D until the interference patterns of all radars are obtained; and the interference decision network is used for realizing a networking multifunctional radar formation anti-interference decision based on a Markov decision. According to the method, the algorithm learning efficiency is effectively improved, the decision-making precision is improved, and the cooperative interference resource scheduling strategy can be quickly and effectively generated under the conditions that the number of jammers is not equal to that of radars and interference resources are limited.

The present invention discloses an electronic countermeasure offline decision-making method, system, device and storage medium. Due to the fact that in a complex electromagnetic environment, the radar state information obtained by the jammer is partially observable and has deviations, the jamming strategy obtained by the general decision-making method is often difficult to guarantee performance when deployed in a real environment due to its limitations. Therefore, the solution provided by the present invention models the electromagnetic environment into an opponent intelligent agent, and the jammer intelligent agent and the opponent intelligent agent are interactively trained to obtain the final jammer intelligent agent. This can better make radar jamming decisions, making the jammer's decision-making in a complex electromagnetic environment more efficient and safe.

The invention discloses a radar anti-interference decision generation method and system based on experience playback and strategy gradient, a medium and a product. The method comprises the following steps: constructing an experience playback pool; a reinforcement learning GPPO model is constructed; training the reinforcement learning GPPO model by using the experience playback pool to obtain a trained reinforcement learning GPPO model; and the trained reinforcement learning GPPO model is used to generate an optimal anti-interference strategy for an interference signal to be suppressed. According to the method, the current state of the linear frequency modulation radar can be better estimated by using the current and historical radar interference data, so that the online generation of the optimal anti-interference decision is realized, and the anti-interference decision generation performance in the dynamic electromagnetic countermeasure environment is also improved.

The invention provides a forest vertical structure information inversion method and system based on radar interferometry, and the method comprises the steps: carrying out the small-view processing of high-resolution SAR interferometry data with a space baseline, calculating the small-view InSAR phase height, extracting the statistical distribution of the small-view interferometry phase height in a set space window, and carrying out the calculation of the small-view interferometry phase height. Generating a small-looks interferometric phase height histogram; establishing an under-forest ground elevation regression estimation model based on the statistical characteristics of the small-looks interferometric phase height histogram, and determining model parameters to invert the terrain; and two canopy top indexes are extracted, two index results are fused by setting a threshold value, and final forest vertical structure information is generated. The method has the advantages that more canopy internal and under-forest information can be captured only by using single-baseline and single-polarization radar interference data, and the sensitivity to a forest vertical structure is improved; and the forest vertical section, the tree height and the terrain can be synchronously inverted.

The invention discloses a radar interference effect evaluation method based on a constraint learning dynamic Bayesian network, is applied to the field of radar interference evaluation, and aims at solving the problem that the accuracy of interference effect evaluation is reduced due to radar detection data missing in a complex electromagnetic environment. Meanwhile, parameter constraints of five types of evaluation indexes and interference effect grades are defined; secondly, constructing a constraint learning dynamic Bayesian network, and learning a conditional probability and a transition probability under a data missing condition; then, proposing a prior constraint expectation maximization algorithm, converting parameter learning into an optimization problem with constraint by combining convex optimization, and overcoming the defects of a traditional expectation maximization algorithm; secondly, a cloud model is introduced to quantify discrete probability distribution into a continuous interference degree value; finally, simulation shows that the method can effectively improve parameter learning stability and evaluation accuracy under the conditions of suppressing and deception jamming and single index deficiency, and provides a reliable scheme for radar jamming effect evaluation in a complex environment.

The invention relates to a glacier change assessment method based on multi-source data fusion, and the method comprises the steps: collecting radar interference measurement data, optical image data and GPS actual measurement data of a target glacier region, and constructing a multi-source data set; the multi-source data set is preprocessed and then fused, and a fused flow velocity field is obtained; performing spatial gradient analysis on the fused flow velocity field to obtain a local change rate of the flow velocity field, judging a sudden motion event based on the local change rate, and obtaining a motion event distribution diagram; constructing a dynamic flow velocity field for a sudden motion area in the motion event distribution diagram, calculating glacier surface stress distribution, and obtaining glacier motion state distribution; and carrying out time sequence analysis on the glacier motion state distribution, obtaining the change trend of the motion state, evaluating the change condition, and completing the change evaluation of the target glacier area. By integrating the multi-source remote sensing data, the temporal-spatial resolution and precision of glacier change monitoring and evaluation are improved.

The invention provides an ISAC system signal detection method based on block diagonal expected propagation, and the method comprises the steps: obtaining a communication signal and a target echo signal transmitted by multi-antenna user equipment through a receiving end, carrying out the whitening of the received signal and noise, and minimizing the interference of a radar to communication; converting the optimized complex signal into a real value system model; initializing a mean vector and a covariance matrix parameter of the expectation propagation algorithm, and setting the number of iterations; calculating diagonal elements of the covariance matrix by adopting a block diagonal Neumann series approximation method; updating a mean vector and adjusting an iteration step length through a dynamic residual factor; and after a preset number of iterations, outputting a final detection signal. According to the method, the complexity is reduced by calculating the diagonal elements of the matrix inversion instead of directly solving the matrix inversion, and a special averaging method is designed for the diagonalization characteristic of the matrix with the similar channel hardening characteristic to optimize the precision.

The invention provides a radar interference signal identification method and system based on a deep fusion neural network, and the method comprises the steps: obtaining an echo signal of a radar, carrying out the generalized S transformation of the echo signal to generate a time-frequency image, carrying out the preprocessing of the time-frequency image, and obtaining a preprocessed time-frequency image; and identifying the preprocessed time-frequency image by using a pre-trained deep fusion neural network model, and outputting an interference signal type. According to the method, the time-frequency image of the radar interference signal with high time-frequency resolution is obtained by adopting generalized S transformation, the time-frequency domain characteristics of the radar interference signal can be flexibly described, and the quality of a learning sample is ensured.

The invention discloses a radar interference robust identification method based on a double-branch residual fusion network, and belongs to the field of radar interference identification. The problems that multi-dimensional information of interference characteristics is difficult to comprehensively capture and weak or variant interference in a complex environment is difficult to accurately recognize in an existing method are solved. The method comprises the steps that radar receiving signals are acquired, and a scrambling signal set is constructed; performing time-frequency processing on the interference signal to generate a time-frequency graph; the method comprises the following steps of: constructing a double-branch residual fusion network which comprises a time domain branch used for respectively carrying out one-dimensional convolution operation on a real part one-dimensional sequence and an imaginary part one-dimensional sequence of a radar signal, and extracting time domain features; the time-frequency domain branch is used for taking the time-frequency graph as input and extracting time-frequency features through the multiple residual modules and the DBB multi-branch convolution module in sequence; the self-adaptive feature fusion module is used for carrying out weighted fusion on the sum to generate fusion features; and inputting the fusion features into a full connection layer, and outputting a radar interference category identification result. The method is mainly used in electronic countermeasure field.

The invention belongs to the technical field of radar interference, and particularly discloses a retrodirective array for cross-eye interference, which comprises at least two antennas, the antennas are configured to receive input signals and forward interference signals based on the input signals, and each antenna is provided with a transponder assembly coupled with the antenna. The transponder assembly can modify the phase of a forwarding signal thereof according to a reference signal, that is, all transponder assembly configurations except at least one transponder assembly can invert the phase of the forwarding signal of an input signal of a coupled antenna thereof according to the reference signal, so as to ensure that all signals are in phase at the source of the input signal. At least one transponder component is configured to introduce at least one phase adjustment to a forwarded signal such that the signal forwarded from the antenna has a phase difference of 170 DEG to 190 DEG and an amplitude difference of less than 3 dB at an input signal source, eliminate inherent delays in a Van Atta cross-eye jammer, introduce angle errors in a threat radar, and achieve conditions required for cross-eye jamming.

In order to solve the defects in the prior art, the invention provides a radar task reliability evaluation method based on gamma distribution in an electromagnetic environment, which comprises the following steps of: (1) calculating the reliability of a radar task according to the distance between preset positions, the signal strength of a reference signal transmitting position and the signal strength of a plurality of reference signal receiving positions; calculating a unit distance signal deviation model and a signal propagation equivalent compensation model; (2) calculating a reference signal receiving position radar interference signal distribution model by adopting the signal propagation equivalent compensation model calculated in the step (1); (3) constructing a radar electromagnetic interference critical threshold logic judgment table by taking different performance requirements required when the radar executes different tasks as indexes; and (4) calculating the radar task reliability by adopting Gamma distribution according to the radar interference threshold obtained in the radar electromagnetic interference critical threshold logic judgment table obtained in the step (3) and the radar interference signal distribution model obtained in the step (1).

The invention discloses a radar interference signal identification method based on multi-modal feature extraction and hierarchical fusion. The method comprises the following steps: firstly, constructing a radar echo signal data set which comprises a plurality of radar interference signals and non-interference radar emission signals; extracting radar signal multi-modal data including IQ, frequency spectrum and amplitude-phase, and performing dimension reduction processing; then, constructing an interference signal identification model which comprises a multi-modal feature extraction module, a three-stage progressive multi-modal feature fusion module and a classification module; the multi-modal data passes through a multi-modal feature extraction module to obtain multi-modal features; the multi-modal features are fused through a three-stage progressive multi-modal feature fusion module, and multi-modal fusion features are obtained; the multi-modal fusion features pass through a classification module to obtain an interference signal identification result; and finally, the model is trained, and the trained model is used for radar interference signal identification. And through the synergistic effect of multi-modal input and three-stage progressive feature fusion, the recognition accuracy is improved.

Methods and systems are disclosed for WLAN devices to select an operating dynamic frequency selection (DFS) channel that minimizes radar interference probability by using assistance information. The assistance information can be a crowd-sourced database of radar zones with geographic markers, including one or more DFS channels used within the radar zones with geographic markers detected by multiple WLAN devices. The WLAN devices can query the crowd-sourced database for nearby radar zones with geographic markers to determine if radar is operating on an overlapping DFS channel so that it can switch to a different channel. In an aspect, the assistance information can be a periodic special action frame broadcast by a WLAN beacon device over the operating channel of the WLAN device. The special action frame can carry information about one or more channels used by nearby radar and a recommended alternative channel for use by the WLAN device.

The invention provides a radar cognitive reconnaissance processing method based on recognition before electromagnetic environment signal detection. The method comprises the following steps: step 1, electromagnetic spectrum panoramic sensing; step 2, clutter noise floor calculation; step 3, identifying communication, radar and interference signals; step 4, generating a signal detection threshold of signal receiving; and step 5, channelized mask generation: under the condition that the channelized receiving and parameter measurement signal capability is limited, the detection, sorting and identification probabilities of a radar signal source are improved by sensing the electromagnetic environment.

The invention discloses an electronic countermeasure off-line decision-making method, system and equipment and a storage medium, and aims to solve the problems that in a complex electromagnetic environment, radar state information obtained by an interference party is partially considerable and has deviation, and when an interference strategy solved by a general decision-making method is deployed in a real environment, the performance is often difficult to guarantee due to limitation, so that the interference strategy is difficult to ensure. According to the scheme provided by the invention, the electromagnetic environment is modeled into the opponent agent, and the interference agent and the opponent agent are subjected to interactive training to obtain the final interference agent, so that a radar interference decision can be better made, and the decision of the interference agent in the complex electromagnetic environment becomes efficient and safe.

The invention relates to a broadband distributed radar interference waveform estimation and suppression method and device, and the method comprises the steps: inputting a receiving sampling signal of a distributed radar, including an interference signal, a target echo and receiving noise; constructing an observation matrix according to the interference parameters; through Fourier transform, converting the time domain signal model of the distributed radar for confronting multiple interferences into a frequency domain model; interference parameters are initialized through design of orthogonal waveforms and cross-correlation analysis, interference signals, a complex coefficient matrix and the interference parameters are optimized through internal and external nested loops, and interference waveform estimation is achieved. And eliminating the interference signal after waveform estimation from the received signal to complete interference suppression. Therefore, the problem that a traditional airspace interference suppression algorithm fails due to interference decorrelation of a current broadband distributed radar system is solved.

The present invention discloses a method for identifying the type of radar interference signal based on a convolutional neural network model, comprising: obtaining an echo time domain sequence and performing time-frequency conversion to obtain an echo frequency domain sequence; performing hierarchical quantization on the echo time domain sequence and the echo frequency domain sequence, respectively, and generating first and second real matrices based on the hierarchical quantization results; concatenating the first and second real matrices and converting them into grayscale images; inputting the grayscale images into a pre-trained convolutional neural network model so that the convolutional neural network model outputs a radar interference signal type identification result; wherein the convolutional neural network model is trained based on multiple sample grayscale images and the radar interference signal type corresponding to each sample grayscale image. Based on the convolutional neural network model, the present invention uses image classification and recognition methods to intelligently identify the type of radar interference signal, and has high generalization ability and recognition accuracy.

The invention discloses an interference communication integrated waveform generation method based on spectrum dispersion-multi-system phase shift keying. The problems of difficult communication information demodulation and high hardware implementation complexity in interference communication integration research are mainly solved. According to the scheme of the invention, the SMSP interference signal is formed by periodically expanding the chirp signal on the time domain, the communication information is modulated on the phase of the chirp signal, and the plurality of chirp signals carrying the communication information can cause dense false target interference at random positions on the radar, so that the interference and communication integrated signal is generated. At a communication receiving end, a first linear frequency modulation signal is adopted to perform carrier synchronization, so that the synchronization effect can be improved by using the high time width-bandwidth product characteristic of the linear frequency modulation signal, and communication information can be demodulated directly by using a coherent demodulation mode; at a radar receiving end, the signal can generate a radar interference effect with dense false target characteristics.

The application discloses a SAR two-dimensional multi-phase segmented modulation interference method based on time modulation metasurface, and relates to the technical field of signal processing. In the application, each metasurface unit is combined with two open reverse and symmetrical metal square rings and varactor diodes, so that the phase, amplitude and polarization state of the incident electromagnetic wave are flexibly regulated and controlled. The dynamic regulation and control capability enables the metasurface to adjust the response to the electromagnetic wave in real time according to the requirements of different application scenarios, so that the electromagnetic environment is accurately controlled. The structure of the application only uses two open reverse and symmetrical metal square rings and varactor diodes, so that the phase, amplitude and polarization state of the incident electromagnetic wave are flexibly regulated and controlled, and the electromagnetic environment is accurately controlled. Compared with the traditional complex time modulation metasurface, the modulation performance of the time modulation metasurface is greatly improved, and high-precision SAR radar interference is realized.

An apparatus for mitigating radar-to-radar interference within a sharable time slot is disclosed. The apparatus includes a wireless transceiver configured to transmit a first radar signal during a first sharable time slot. The first radar signal has a start time delayed by a first time delay relative to a start time of the first sharable time slot. In addition, the wireless transceiver is configured to receive a first received signal during the first sharable time slot, and to detect a potential object based on the first received signal. Further, the wireless transceiver is configured to transmit a second radar signal using the first antenna during a second sharable time slot. The second radar signal has a start time delayed by the second time delay relative to a start time of the second sharable time slot. The second time delay is different from the first time delay based on the detection of the potential object.