296 results about "Electronic countermeasure" patented technology

The invention discloses an SDR-oriented heterogeneous task scheduling and transmission system and method.The system comprises a heterogeneous platform composed of an ARM and an FPGA, a dynamic scheduling decision engine is arranged in the ARM and receives a task feature vector and a platform state parameter set as input, and the dynamic scheduling decision engine evaluates income and cost of task allocation to the ARM or the FPGA for execution to make a task allocation decision; the data generated in the task execution process or the execution completion result are interacted and synchronized between the ARM and the FPGA through the heterogeneous shared memory area HSM. The method realizes reduction of signal processing pipeline delay, improvement of data throughput and optimization of system power consumption, and is suitable for communication, electronic countermeasure and other scenes needing high-performance real-time signal processing.

The invention discloses a multi-mode SAR interference method and device, and belongs to the field of radar electronic countermeasure, and the method comprises the steps: respectively calculating a product matrix of each interference mode in an interference preparation stage; after entering a reconnaissance stage, intercepting a first section of receiving signal, judging whether the first section of receiving signal is an SAR signal or not, estimating parameters of the SAR signal, and extracting one frame of complete SAR pulse signal as a reference signal; intercepting a second section of receiving signal, and judging whether to enter an interference stage or not according to the PRF; after entering the interference stage, receiving a part of SAR pulse signals and recovering the part of SAR pulse signals into complete SAR pulse signals; multiplying the complete SAR pulse signal by the product matrix to obtain a baseband interference signal; and modulating the baseband interference signal into a radio frequency interference signal and transmitting the radio frequency interference signal. According to the invention, multiple modes of interference such as false target implantation, pseudo radio frequency interference and scattered wave interference can be realized, the interference signal modulation complexity is low, and the requirement of the interference device for the isolation of the transceiving antenna is low.

The invention provides a cooperative reconnaissance and electronic countermeasure control system and method based on multiple unmanned aerial vehicles. The cooperative reconnaissance and electronic countermeasure control system comprises five modules: a communication module, a task decomposition module, a reinforcement learning decision module, a cooperative optimization module and a task evaluation module. The communication module ensures data synchronization and safe communication between the unmanned aerial vehicles through a self-organizing network and an encryption anti-interference technology. And the task decomposition module is used for refining the electronic confrontation task into reconnaissance and interference subtasks according to the task type, and adjusting the priority in real time according to the battlefield environment. The reinforcement learning decision module adopts a DQN and PPO deep reinforcement learning algorithm to optimize a reconnaissance and interference strategy, and ensures optimal task execution. And the collaborative optimization module coordinates task allocation and flight paths of the unmanned aerial vehicle through multi-agent reinforcement learning and a bee colony algorithm, and solves the problem of task conflict. And the task evaluation module monitors a task state in real time and feeds back an adjustment strategy. According to the method, battlefield changes can be flexibly coped with, resource scheduling is optimized, task execution efficiency is improved, anti-interference capability is enhanced, and stability and safety of tasks are guaranteed.

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

The invention discloses an unknown interference efficiency evaluation method and system for an irregular electronic countermeasure scene, in the electronic countermeasure scene, a deep reinforcement learning model based on a MuZero algorithm is introduced, and the greatest advantage of the MuZero algorithm is that decision optimization and efficiency evaluation in a complex environment can be completed without explicit rule modeling. By learning environment dynamics in a potential state space, the model can adapt to dynamic changes of an interference mode and a radar anti-interference strategy, and dependence of a traditional method on rule hypothesis is avoided. Besides, real-time strategy optimization of the model is realized in combination with Monte Carlo tree search (MCTS) and deep learning, the optimal interference strategy can be quickly generated in confrontation scenes with unknown rules and complex environments, and the intelligence and efficiency of the electronic confrontation system are remarkably improved.

The invention belongs to the technical field of radar electronic countermeasures, and discloses an FPGA-based Doppler flicker interference generation method, which comprises the following steps that: an upper computer issues system parameters and Doppler flicker interference strategy parameters to an FPGA; the signal transmitting and collecting module of the FPGA receives and processes the radar radio frequency signal to obtain a radar baseband signal and stores the radar baseband signal; the control unit extracts a radar baseband signal according to the system parameters, and generates alternately changing Doppler frequency control words according to strategy parameter control; the extracted radar baseband signal or synchronization information thereof and a frequency control word are sent to a multi-path parallel DDS unit to generate a Doppler flicker interference baseband signal; the interference baseband signal is processed by the signal emission and acquisition module and then is emitted to form an interference radio frequency signal. The method is realized by utilizing the FPGA, Doppler flicker interference can be flexibly and efficiently generated, effective cheating is caused to radar speed tracking, and the method has the advantages of high response speed, high flexibility and good interference effect.

The invention relates to a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

A portable multifunctional AI enabling electronic countermeasure system relates to the technical field of electronic countermeasure, and comprises a host housing, the bottom of the host housing is provided with a tripod, and the surface of the host housing is provided with a plurality of broadband antennas and a reference omnidirectional antenna; a plurality of transceiving front-end assemblies, a receiving front-end assembly and a signal processing unit are arranged in the host shell, the transceiving front-end assemblies are electrically connected with the broadband antennas in a one-to-one correspondence manner, and the reference omnidirectional antenna is electrically connected with the receiving front-end assembly; the plurality of transceiving front-end assemblies and the receiving front-end assembly are electrically connected with the signal processing unit; the signal processing unit is used for processing the radio frequency signals output by the plurality of transceiving front-end assemblies and the receiving front-end assembly by adopting an AI algorithm model to generate corresponding interference signals, and the interference signals are sent to the broadband antenna through the plurality of transceiving front-end assemblies; the method is used for solving the problems of weak interference capability and slow deployment speed in the prior art.

The invention discloses an adaptive electronic countermeasure interference source dynamic scheduling method based on spectrum sensing, and the method comprises the following steps: S1, collecting broadband spectrum data in an electromagnetic environment in real time through a broadband receiver, and transmitting the broadband spectrum data to a plurality of parallel shallow convolutional neural networks for feature extraction; s2, extracting the frequency characteristic, the power intensity, the signal-to-noise ratio and the current interference source state of the target signal in real time, and constructing a multi-dimensional state space; s3, constructing a multi-head depth Q network model based on the multi-dimensional state space, and outputting a decision instruction through parallel training; s4, combining the decision instruction into an interference decision instruction and sending the interference decision instruction to an interference source in real time; s5, the interference source executes the interference task and feeds back the interference effect; and S6, dynamically adjusting network parameters according to interference effect feedback. The method improves the interference decision precision and response efficiency, and is suitable for electronic countermeasure tasks in a complex electromagnetic environment.

The invention discloses a non-standard electromagnetic target detection and smart interference integrated system based on a ZYNQ architecture in the technical field of electronic countermeasure and communication systems, and the system comprises an antenna unit which is used for receiving and transmitting electromagnetic signals of a wide frequency band; the gain module is used for amplifying the received and transmitted signals and improving the signal quality and the detection sensitivity; the radio frequency front end is used for completing up-down frequency conversion and analog-to-digital conversion of signals and transmitting and receiving operation of radio frequency signals and providing necessary signal conversion and processing functions for the system; the PC end is used for issuing a control instruction and adjusting the gain of the system so as to realize amplification and flexible adjustment of the signal; the FPGA signal processing unit is used for completing signal generation, receiving and processing and subsequent signal analysis and interference algorithm execution based on an FPGA platform, the detection precision and the processing speed of the system are remarkably improved, a non-standard electromagnetic target in a complex electromagnetic environment can be efficiently recognized and dealt with, and interference response is flexibly carried out.

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 invention discloses a secondary modulation signal identification method and system based on joint model deep learning, and relates to electronic countermeasure, wireless communication and signal processing technologies, and the method comprises the steps: obtaining a sample secondary modulation signal, and carrying out the preprocessing; for the preprocessed sample secondary modulation signal, extracting a spatial-temporal characteristic sample based on signal reconstruction and phase space statistical analysis permutation entropy; a lightweight deep learning model is adopted to jointly construct a data local spatial characteristic perception model and a time sequence backtracking control model, and the extracted spatial-temporal characteristic samples are utilized to train the joint model; and extracting spatial-temporal characteristics of the secondary modulation signal of the target, and outputting an identification result by using the trained joint model. The method can solve the problems that a secondary modulation signal method in the field of signal and information processing is low in recognition accuracy and large in calculation amount, and the number of needed prior samples is large.

The invention discloses a signal reconnaissance, positioning and interference method based on deep learning, and belongs to the technical field of electronic countermeasures. The method comprises the following steps: deploying distributed reconnaissance nodes to obtain broadband electromagnetic signals; generating a spectrogram through time-frequency transformation, and identifying a fixed frequency / frequency hopping signal based on a deep learning model; fusing multi-node reconnaissance results to realize target situation awareness; the optimal positioning cluster is selected through dynamic networking, and the target position is calculated in combination with the arrival angle and time difference information; generating an optimal interference strategy based on the target situation and the position trajectory; networking cooperative interference is implemented; behavior parameters are collected in real time, the interference effect is evaluated through a machine learning model, and the strategy is dynamically adjusted. According to the method, the technical effects of high-efficiency and self-adaptive signal reconnaissance, positioning and interference in a complex electromagnetic environment are achieved.

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 provides a system for precise targeting in GPS-denied environments using a plurality of Computer-Readable Image Markers (CRIMs) and imaging devices. CRIMs, such as Apriltags, are deployed around a target area by an aerial vehicle. Each CRIM includes a unique code and an anchor that absorbs moisture to stabilize its position. High-altitude imaging devices capture the CRIMs' positions relative to the target, and this data is processed to create a map for autonomous navigation. An attack drone uses this map, identifying unmoved CRIMs to adjust its course and engage the target accurately, even in contested environments with electronic countermeasures. The method leverages robust image recognition, reducing computational load and enhancing reliability. The system offers a cost-effective solution for military and humanitarian applications where GPS is compromised.

The invention discloses a multichannel broadband receiver, and belongs to the technical field of microwave radio frequency and electronic control, the multichannel broadband receiver comprises a multichannel down-conversion assembly, a local oscillator power component, a power interface control panel and a VPX connector, the down-conversion assembly adopts a cavity-divided integrated structure, multiple identical radio frequency links are arranged in the same cavity in parallel, and the VPX connector is connected with the local oscillator power component. Down conversion from a 2-18GHz radio frequency signal to a 1.3-2.3 GHz intermediate frequency signal is realized through two-stage frequency mixing; the power supply interface control board takes an FPGA as a core, integrates the functions of multi-path power supply conversion, driving isolation, state monitoring and the like, and realizes channel gain power failure storage and calling, BIT self-inspection, nanosecond rapid turn-off and multi-parameter real-time monitoring through software and hardware cooperation. Through collaborative innovation of the structure, circuit and control, high integration, high channel consistency, high isolation and high intelligence of the multi-channel receiver are realized, and the multi-channel receiver is suitable for the fields of high-performance radars, electronic countermeasures and the like.

The invention belongs to the technical field of electronic countermeasures, discloses a small approaching type unmanned aerial vehicle flight path deception feasibility evaluation and correction method, and discloses a flight path plan making method for implementing flight path deception interference on a radar by using a small approaching type unmanned aerial vehicle, which comprises the steps of expected flight path feasibility evaluation and expected flight path correction. The advantages of the small approaching type unmanned aerial vehicle are developed to the maximum extent. The method is suitable for flight path deception of a single unmanned aerial vehicle to a single radar and flight path deception of an unmanned aerial vehicle group to a radar net. According to the flight path plan making method for implementing flight path deception on the radar by using the unmanned aerial vehicle, power and speed constraints of the unmanned aerial vehicle are considered, feasibility evaluation is carried out on the designed expected flight path, a set of correction strategies is provided for an infeasible area, it is guaranteed that the corrected flight path is close to the original expected flight path as much as possible, and the flight path deception rate is improved. And the effect of the unmanned aerial vehicle is maximized.

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 specifically provides a low-cross-polarization ultra-wideband wide-angle scanning low-profile tight coupling phased-array antenna based on metal grid loading, and belongs to the technical field of microwave antennas. The three-order Chebyshev impedance transformer comprises a C-shaped ring metasurface loading wide-angle matching layer, a folded dipole, a double-Y balun, a three-order Chebyshev impedance transformer, a coaxial feed port, a metal floor, a metal grid, a grounding metal sheet, a resistive film, a dielectric layer and a metal chip resistor. Different polarization electromagnetic waves are regulated and controlled through the metal grid, the resistive film is introduced to absorb cross polarization components, the front-to-back ratio is improved, and the cross polarization level is smaller than or equal to-30 dB; the working bandwidth is expanded to 5.6: 1 under the condition that the profile is only 0.09 lambda by combining the size design of the non-uniform unit and the gradient balun feed, and the scanning of the wide angle of + / -60 degrees of an E surface and an H surface is supported. The invention has the advantages of compact structure, high polarization purity and wide-angle stability, and is suitable for 5G / 6G communication, radar and electronic countermeasure systems.

The invention belongs to the technical field of electronic countermeasure and target positioning, and relates to a TDOA radiation source tracking and trajectory prediction method based on EKF. The method comprises the following steps: S1, setting N sensor nodes to measure a TDOA observation value; s2, establishing a radiation source motion state space model; s3, calculating a Jacobian matrix to linearize the nonlinear observation equation; s4, executing an EKF prediction and updating step, and updating state estimation by using a TDOA observation value; s5, predicting a future trajectory based on the current state; and S6, performing loop execution to realize continuous tracking. According to the method, the nonlinear problem of TDOA positioning is solved through the EKF algorithm, high-precision tracking and trajectory prediction of the high-speed moving radiation source are achieved, good robustness and real-time performance are achieved, the algorithm complexity is moderate, and the method is suitable for accurate positioning of the moving radiation source in an electronic countermeasure system.

The invention relates to the technical field of radars, in particular to a low-interception-probability radar waveform design method and device, and the method comprises the steps: obtaining a preset MDP tuple; constructing a Markov decision model framework based on a preset MDP tuple, wherein the Markov decision model framework comprises a design framework corresponding to the power adopted for constructing a radar waveform and the complexity of a radar waveform intra-pulse modulation pattern; performing reinforcement learning on the Markov decision model framework in a strategy optimization direction taking maximization of long-term accumulated rewards as a target to obtain a reinforcement learning optimization model of radar waveform design; and solving the reinforcement learning optimization model of the radar waveform design by using a model-free Q-learning algorithm to obtain the design parameters of the radar wave, thereby realizing the intelligent design of the low-interception probability radar waveform in the complex electronic countermeasure environment, and improving the concealment and detection performance balance capability of the radar in the unknown countermeasure environment.

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 discloses a direction finding method based on an unmanned aerial vehicle cluster under a multi-factor error condition, and the method comprises the following steps: S10, in a laboratory, respectively considering a position error, a radio frequency channel amplitude-phase error, a nonlinear processing error and a coupling mutual interference error through a control variable method, and then constructing a network model considering the four errors; s20, deploying an unmanned aerial vehicle cluster carrying an electronic countermeasure reconnaissance load, sensing and receiving signals from each target radiation source, and recording data; s30, performing error correction on the data received in the step S20 by using the network model in the step S10; s40, after the error correction data in the step S30 are received, performing virtual-real separation and then entering a neural network; s50, sequentially processing the data in the neural network for six times to obtain an output function, combining virtual and real parts of the output function to obtain a complex function, multiplying the complex function with the steering vector to obtain a spatial spectrum, and searching peaks according to the spatial spectrum to carry out direction finding on each radiation source; the invention further discloses a corresponding system. The accuracy, reliability and flexibility of electronic countermeasure reconnaissance are improved, and the direction finding process is simplified.

The invention relates to the field of high-frequency electronics, and discloses an ultra-wideband traveling wave tube which comprises a focusing control electron gun, a slow wave system and a three-stage depressed collector. The focusing control electron gun is used for providing stable electron beams and realizing effective focusing and acceleration of the electron beams through a cathode assembly, a heating wire, a bunching electrode and an anode assembly which are accurately designed; the slow wave system is composed of an input high frequency and an output high frequency, high-efficiency energy exchange between an input signal and an electron beam is ensured through a conical spiral line and a composite tube shell structure, and therefore signal amplification is achieved. The three-stage depressurization collector effectively recovers residual electron beams through a step-by-step depressurization design, energy loss is reduced, and the total efficiency of the traveling wave tube is improved. The traveling wave tube can provide output power exceeding 100 W in the frequency band of 2-18 GHz, the gain is larger than 25 dB, and the total efficiency is larger than 30%. Due to the excellent performance, the method can be widely applied to the fields of communication, radar, electronic countermeasures and the like.

The invention relates to the technical field of electronic countermeasures, in particular to an unmanned aerial vehicle group dynamic interception method and system based on distributed interference array collaboration. According to the method, an interference node cluster with autonomous logic association is dynamically deployed in a defense domain, and an elastically reconfigurable distributed interference field domain is formed through a software defined radio architecture; time-varying spectrum features are extracted through an adversarial neural network, and a dynamic threat fingerprint database is constructed; generating an interference vector with frequency band following and power self-adaption based on the dynamic threat fingerprint database, and updating the feature weight of the dynamic threat fingerprint database in real time according to interference efficiency feedback; and according to the situation evolution trend of the dynamic threat fingerprint database, a hierarchical interference wave sequence is generated based on a multi-agent consensus protocol, and a space-time coupled interception chain is formed. Through the method, the whole-process dynamic confrontation capability of the unmanned aerial vehicle group is realized, the efficient interception performance is maintained in a complex electromagnetic environment, and meanwhile, the interference risk on legal communication is reduced.

The invention discloses a spectrum signal detecting and positioning method and system based on unsupervised reinforcement learning, and belongs to the technical field of wireless spectrum sensing and electronic countermeasure. In order to solve the problems of low-power and low-SNR signal automatic detection and positioning in a complex electromagnetic environment, the method comprises the steps of nonlinear normalized spectrum mapping, anchor frame heuristic initialization, multi-source state coding and attention fusion, PPO-based continuous action reinforcement learning, a multi-dimensional learnable reward function and a multi-round dynamic detection strategy. And adaptive detection and track positioning of the frequency spectrum signal are realized. According to the method, high-precision detection and time-frequency track positioning of various types of signals in a low SNR environment can be realized without manual labeling, and the robustness and universality of spectrum sensing are improved.

The invention discloses a semi-supervised radiation source individual identification method based on multi-round clustering and pseudo label updating, which is characterized in that on the basis of a structured slice of a radiation source signal, a deep neural network is adopted to carry out feature extraction and classification prediction on a sample, and network training is carried out through multi-round change trajectory clustering and a pseudo label stability judgment mechanism; the method comprises the following steps: setting a trigger condition of pseudo-label generation as stable convergence of training loss, tracking a historical prediction trend of a sample, screening out a sample set with consistent category evolution trajectory and discrimination as a pseudo-label candidate, expanding a supervised data volume through a pseudo-label iteration updating strategy and a training sample set dynamic reconstruction mechanism, and obtaining a pseudo-label generation result. And finally, based on a joint loss optimization strategy, realizing comprehensive identification of all unknown samples in continuous iteration. According to the method, the adaptive capacity of the identification model to unknown category and weak label data is improved, and the method is suitable for complex wireless environments such as electronic countermeasure, frequency spectrum monitoring and signal traceability needing high-robustness identification capacity.

The invention, which relates to the radar simulation and electronic countermeasure field, provides a multi-mode broadband radar simulation electronic target system and method for an unmanned aerial vehicle, and the system comprises an upper computer, a ZYNQ signal processing module, an ADRV9009 radio frequency transceiver module and a transmitting module which are connected in sequence. According to the invention, a ZYNQ signal processing module and an ADRV9009 radio frequency transceiver module are taken as hardware bases, multimode radar simulation is taken as a software architecture, various radar waveforms are generated and adjusted according to key characteristic indexes of a radar, inter-pulse characteristics, intra-pulse characteristics and frequency tuning are supported, and radar signals in a complex electromagnetic environment are accurately simulated.

The invention discloses a radar communication integrated radiation source simulation system and method. The system comprises an upper computer control unit, a signal processing unit, an up-conversion unit and an antenna unit. An unknown radar or communication signal is analyzed by adopting a signal characteristic analysis method based on phase difference, whether the signal is a complex signal is judged, the waveform of the signal is stored and generated by adopting a DDWS technology aiming at the unknown complex signal, the waveform of the signal is directly generated by adopting a modulation module of a signal processing unit aiming at a conventional signal, and the waveform of the signal is directly generated by adopting a modulation module of a signal processing unit aiming at a known complex signal. The waveform of a fixed part in a signal is synthesized by adopting a DDWS technology, a modulation module of the signal processing unit is used for generating a non-fixed part, and the fixed part and the non-fixed part are combined according to a signal message format time sequence, so that the simulation of a complex signal is realized, and the requirement of current electronic countermeasure training on radar communication integrated radiation source simulation is met.

The present invention relates to a method, device, electronic device, and readable storage medium for generating a baseband signal, belonging to the technical field of electronic countermeasure simulation training signal generation technology, and solving the problem of low baseband signal generation efficiency in the prior art. The method comprises: generating multiple alternative baseband sampling rates based on a final sampling rate; among the alternative baseband sampling rates, determining the corresponding baseband sampling rate according to the characteristics of each signal, and forming a baseband sampling group with signals having the same baseband sampling rate; and each baseband sampling group independently generates a signal. The technical solution of the present invention improves the efficiency of baseband signal generation and saves storage and communication resources.