Aiming at the problem of low spectrum utilization of the traditional spectrum allocation scheme in a large-scale and high dynamic electromagnetic spectrum warfare system,intelligent spectrum allocation technology research is carried out.In this paper,first,we construct a complex and highly dynamic electromagnetic spectrum combat scenario,and under the coexistence conditions of multiple types of equipment such as radar,communication and jamming,we model the spectrum allocation of the complex electromagnetic environment as an optimization problem to maximize the number of access devices.Second,an intelligent spectrum allocation algorithm based on clustering assistance is proposed.Aiming at the centralized resource allocation algorithm facing the problem of exploding action space dimensions,a multi-DDQN network is used to characterize the decision-making information of each node.Then based on the elbow law and K-means++ algorithm,a multi-node collaborative approach is proposed,where nodes within a cluster make chained decisions by sharing action information and nodes between clusters make independent decisions,assisting the DDQN algorithm to intelligently allocate resources.By designing the state,action space and reward function,and adopting the variable learning rate to realize the fast convergence of the algorithm,the nodes are able to dynamically allocate the multidimensional resources such as frequency/energy according to the electromagnetic environment changes.Simulation results show that under the same electromagnetic environment,when the number of nodes is 20,the number of accessible devices of the proposed algorithm is increased by about 80% compared with the number by the greedy algorithm,and about 30% compared with that by the genetic algorithm,which is more suitable for the spectrum allocation of multi-devices under dynamic electromagnetic environment.
Operational pattern recognition is one of the important means in the field of intelligence reconnaissance and electronic countermeasures,which is to determine the function and behavior of radar through signal processing and analysis.With the diversification of modern airborne radar functions,the corresponding signal styles are becoming more and more complex,and the increasingly complex reconnaissance environment also leads to the uneven quality of reconnaissance signals,which brings about great difficulties to the traditional operational pattern recognition methods.To solve this problem,based on the existing work pattern recognition methods,a new work pattern recognition method is proposed,which integrates parameter feature recognition and D-S evidence theory recognition.First,for the radiation source characteristic signals processed by each reconnaissance plane,the feature parameter recognition algorithm is used to quickly obtain the working mode information,and the recognition results are verified by the D-S evidence theory.Second,for the signal that can not be recognized by a single platform,the method of D-S evidence theory fusion recognition is used to distinguish the working mode.From the theoretical analysis,it can be concluded that the algorithm has the advantages of fast operation speed and simple structure,and that the new fusion recognition method can improve the recognition accuracy of the working mode.Finally,the feasibility of the method is verified by simulation.
The passive radar system realizes the target detection by receiving the direct wave signal from the emitter and the target echo signal.The cross ambiguity function is an important means to improve the coherent accumulation of the echo signal.However,the echo signal received by the passive radar is very weak,so it is necessary to increase the accumulation time to improve the estimation accuracy.When the target speed is fast,the frequency search range increases.In order to achieve a range of target detection requirements and take into account the real-time performance of data processing,it is of great significance to study the fast calculation method of the cross ambiguity function,and due to the objective requirements of long-time accumulation and large-scale time-frequency search,the computation of the cross ambiguity function is huge,which makes it difficult for the traditional accelerated calculation method based on ergodic search to meet the real-time requirements of system processing.In order to improve the efficiency of cross ambiguity function optimization,a time-frequency difference calculation method based on multi-group feature optimization is proposed in this paper.By deeply analyzing the characteristics of typical digital TV signals,a two-stage cross ambiguity intelligent optimization fast calculation method based on target characteristics is designed in the framework of particle swarm optimization theory.By designing an effective search strategy,this method introduces the multi-population iteration mechanism and shrinkage factor,which avoids the disadvantages of the traditional method of redundant computation.On the premise of ensuring the calculation accuracy,the time-frequency point calculation is greatly reduced,and the search efficiency of cross ambiguity function is improved.
Nowadays,severe electromagnetic circumstances pose a serious threat to electronic systems.The excellent performance of gallium nitride based high electron mobility transistors makes them more suitable for high power and high frequency applications.With the continuous improvement in the quality of crystal epitaxial material and device manufacture technology,gallium nitride semiconductor devices are rapidly developing towards the direction of high power and miniaturization,which challenges the reliability and stability of devices.In this paper,the damage effects of the high power electromagnetic pulse(EMP) on the enhanced GaN high-electron-mobility transistor(HEMT) are investigated in detail.The mechanism is presented by analyzing the variation of the internal multiple physical quantities distribution in the device.It is revealed that the device damage is dominated by the different thermal accumulation effect such as self-heating,avalanche breakdown and hot carrier emission during the action of the high power EMP.Furthermore,the multi-scale protection design of the GaN HEMT against the high power electromagnetic interference(EMI) is presented and verified by simulation study.The device structure optimization results demonstrate that a proper passivation layer which enhances the breakdown characteristics can improve the anti-EMI capability.The circuit optimization presents the influences of external components on the damage progress.It is found that the resistive components which are in series at the source and gate will strengthen the capability of the device to withstand high power EMP damage.All above conclusions are important for device reliability design using gallium nitride materials,especially when the device operates under severe electromagnetic circumstances.
With the maturity and development of the autonomous navigation flight technology for the unmanned aerial vehicle(UAV),the phenomenon of the unauthorized UAV flying in controlled airspace appears,which brings a great hidden danger to personal safety and causes a certain degree of economic losses.The research of this paper is on improving the effectiveness of adaptive measurement and control and navigation interference in the unknown situation of UAV flight control on the basis of identifying the UAV flight status and real-time evaluation of countermeasure effectiveness,and finally realizing the intelligent countermeasure game between the non-intelligent UAV based on the combination of remote communication interference and navigation and positioning interference.In this paper,a game model of the anti-UAV system(AUS) and UAV confrontation is developed based on the original units of radar detection,GPS navigation positioning,UAV remote communication suppression jamming and GPS navigation suppression and spoofing.The mathematical model is constructed by using deep reinforcement learning and the Markov decision process.Meanwhile,the concept of situation assessment ring for the classification of the UVA flight status is proposed to provide basic information for network sensing jamming effectiveness.The near-end strategy optimization algorithm,maximum entropy optimization algorithm and actor-critic algorithm are respectively used to train the constructed intelligent AUS for many times,and finally the network parameters are generated to generate the intelligent interference combination sequence according to the UAV flight state and countermeasures efficiency.The intelligent interference combination sequences generated by various deep reinforcement learning algorithms in this paper all achieve the initial goal of deceiving UAVs,which verifies the effectiveness of the anti-UAVs system model.The comparison experiment shows that the proposed situation assessment loop is sufficient and effective in the aspect of AUS sensing interference effectiveness.
A radar behavior mode recognition framework is proposed aiming at the problems of difficult feature extraction and low recognition stability of the radar signal under a low signal-to-noise ratio,which is based on depth-wise convolution,multi-scale convolution and the self-attention mechanism.It improves the recognition ability in complex environment without increasing the difficulty of training.This algorithm employs depth-wise convolution to segregate weakly correlated channels in the shallow network.Subsequently,it utilizes multi-scale convolution to replace conventional convolution for multi-dimensional feature extraction.Finally,it employs a self-attention mechanism to adjust and optimize the weights of different feature maps,thus suppressing the influence of low and negative correlations in both channels and the spatial domains.Comparative experiments demonstrate that the proposed MSCANet achieves an average recognition rate of 92.25% under conditions of 0~50% missing pulses and false pulses.Compared to baseline networks such as AlexNet,ConvNet,ResNet,and VGGNet,the accuracy has been improved by 5% to 20%.The model exhibits stable recognition of various radar patterns and demonstrates enhanced generalization and robustness.Simultaneously,ablation experiments confirm the effectiveness of deep grouped convolution,multi-scale convolution,and the self-attention mechanism for radar behavior recognition.
Aiming at the covert communication scenario of an unmanned aerial vehicle(UAV) jammer assisted cognitive radio network,a transferred generative adversarial network based resource optimization algorithm is proposed for the UAV’s joint trajectory and transmit power optimization problem.First,based on the actual covert communication scenario,the UAV jammer assisted cognitive covert communication model is constructed.Then,a transferred generative adversarial network based resource allocation algorithm is designed,which introduces a transfer learning and generative adversarial network.The algorithm consists of a source domain generator,a target domain generator,and a discriminator,which extract the main resource allocation features of legitimate users not transmitting covert message by transfer learning,then transform the whole covert communication process into an interactive game between the legitimate users and the eavesdropping,alternatively train the target domain generator and discriminator in a competitive manner,and achieve the Nash equilibrium to obtain resource optimization solution for the covert communications.Numerical results show that the proposed algorithm can attain near-optimal resource optimization solution for the covert communication and achieve rapid convergence under the assumptions of knowing the channel distribution information and not knowing the detection threshold of the eavesdropper.
Precision jamming is a new concept in the field of electronic warfare.The core idea is to adopt a group of drone swarms equipped with jammers as ultra-sparse arrays to transmit the jamming waveform,which aims to implement blanket jamming to the opponent equipment in the spatial domain precisely and ensures that the friendly equipment is not being affected.However,the existing methods apply only to some specific scenarios,and they need to be improved in computational efficiency.In this case,this paper proposes an efficient waveform design algorithm based on the complex circle manifold to improve the computational efficiency,which can control the energy level in the target and friendly regions according to the practical requirement.First,we establish a novel multi-objective optimization problem(MOP) with unimodular constraints according to the precision jamming geometric model and the worst case of jamming energy distribution in the spatial domain.Then,we adopt the Lp-norm to smooth and approximate the minimax objective function.Finally,the MOP with unimodular constraints is viewed as an unconstrainted problem under the complex circle manifold from the perspective of the Riemann geometry,with the Riemannian Conjugate Gradient(RCG) algorithm employed to solve the problem efficiently.Simulation results are provided to demonstrate that the proposed algorithm can control the energy level in different regions by adjusting the regularization parameter,which meets the requirement of different scenarios and tasks of precision jamming.Moreover,it has a lower computational complexity and improves the computational efficiency for the precision jamming waveform design as compared to the existing methods.
Precision jamming technology is one of the hot research directions in current new electronic warfare.To solve the problem of accurately adjusting the spatial and frequency domain distribution characteristics of jamming power,a precision jamming waveform design method based on the alternating multiplier method is proposed.First,a mathematical model for the optimization problem of designing constant-modulus precision jamming waveforms under the joint optimization objective of spatial and frequency domain characteristics is presented.Furthermore,by introducing variables,the non-convex quartic term contained in the original objective function is transformed into a quadratic term,enabling the optimization problem to be solved by the alternating direction method of multipliers.Based on theoretical derivation,the optimal closed form solution for each iteration of the alternating direction method of multipliers is obtained,thereby reducing the computational complexity of the algorithm.Simulation experiments show that compared to existing precision jamming waveform design methods that only optimize the spatial jamming power distribution,the precision jamming waveform designed by this algorithm has better power spectrum distribution characteristics in the synthesized signal within the preset jamming area,which is more in line with the requirements of actual jamming tasks;meanwhile,compared to existing joint optimization algorithms in the spatial and frequency domains,the algorithm proposed in this paper considers the waveform constant-modulus constraint,which is more in line with the needs of engineering implementation.Moreover,the proposed algorithm is lower in computational complexity and it can further reduce the computational time through parallel computing.
In estimation of the complex high-resolution range profile(HRRP) of a ship in the sea clutter,the inaccurate target scattering model leads to the performance degradation of the existing linear program-based(LP-based) sparse recovery method and the sparse recovery method via iterative minimization(SRIM).In this paper,a bi-iterative optimization algorithm is proposed to solve this problem.The algorithm first adopts the LP-based sparse recovery method or SRIM method to estimate the complex HRRP of the ship at a given HRRP model and then tunes the position of the scatterer at each range cell by using the quasi-Newton algorithm to construct a more refined target scattering point model with the same scale.The bi-iterative process above is repeated until the recovery error of the ship HRRP meets the demand specified in advance.Through simulation and measured data experiments,the performance of ship complex HRRP estimation and that of radial size estimation with several sparse recovery methods are analyzed and compared.Experimental results show that the proposed bi-iterative optimization algorithm attains less estimation errors in ship complex HRRP and radial size than the LP-based sparse recovery method and SRIM method and requires much less computational time when it attains a comparable performance with the LP-based sparse recovery method using the oversampled HRRP model.
The space-based photoelectric detection unit is vital in satellite identification and positioning.It has a large field of view,a small load,and flexible maneuvering properties.However,the computational capability of the CPU mounted on an on-orbit satellite could be much higher,which can hardly afford the necessity of deep learning networks.In this paper,we analyze the character of space targets deeply and designed a lightweight real-time processing algorithm.We specifically design feature extractors for the line and satellite contour patterns in the algorithm and propose a minimum bounding box calculation strategy.The algorithm is tested on the simulation dataset and verified on the images captured by the physical emulation platform.Testing results demonstrate the effectiveness of our algorithm.The detecting accuracy of our algorithm is better than YOLOv5n,and the computational load is only 10% that of the competitive methods.We transplant the algorithm to an on-orbit real-time processing platform.The positioning speed reaches 5 120×5 120@5fps.The accuracy for angle measurement based on the centroid positioning results is more significant than 0.05°,which meets the requirement for real application systems.
While deep neural networks have achieved an impressive success in computer vision,the related research remains embryonic in radio frequency signal processing,i.e.,a vital task in modern wireless systems,for example,the electronic reconnaissance system.Noise corruption is a harmful but unavoidable factor causing severe performance degradation in the signal processing procedure,and thus has persistently been an intractable problem in the radio frequency domain.For example,a classifier trained on the high signal-to-noise ratio(SNR) data might experience a severe performance degradation when dealing with low SNR data.To address this problem,in this paper we leverage the powerful data representation capacity of deep learning and propose a Generative Adversarial Denoising and classification Network(GADNet) for radar signal restoration and a classification task.The proposed GADNet consists of a generator,a discriminator and a classifier fulfilling an end-to-end workflow.The encoder-decoder structure generator is trained to extract the high-level features and recover signals.Meanwhile,it fools the discriminator’s judges by bewildering the denoising results coming from the clean data.The classification loss from the classifier is adopted jointly to the training procedure.Extensive experiments demonstrate the benefit of the proposed technique in terms of high-quality restoration and accurate classification for radar signals with intense noise.Moreover,it also exhibits superior transferability in low SNR environments compared to the state-of-the-art methods.
The Winograd transposed convolution algorithm is a widely used convolution acceleration method for Field Programmable Gate Array(FPGA).It can solve the zero-padding problem of transposed convolution by performing the Winograd convolution after grouping.However,this method requires grouping operation on the input feature map and convolution kernel,and needs to reorganize the operation results to generate a complete output feature map.The complex calculation of element coordinates increases the difficulty of design.To solve the above problems,a Winograd transposed convolution method based on the unified transformation matrix is proposed,which uses the unified transformation matrix instead of grouping the input feature map and convolution kernel,and effectively solves the problems of overlapping summation,zero padding,convolution kernel inversion,decomposition and reorganization.And under the guidance of the Winograd transpose convolution method based on the unified transformation matrix,combined with data reuse,the double buffer and the pipeline,the design of a transposed convolution accelerator on FPGA is completed.The Gaussian-Poisson generative adversarial network is selected for experimental verification,and compared with the mainstream transposed convolution method.Experimental results show that the proposed method can effectively reduce the resource consumption and power consumption,and that the effective performance of the accelerator is 1.13x~23.92x higher than that of the existing transposed convolution methods.
The current car following control methods based on the control theory rely on models for both car speed and distance,which suffer from a lack of generalization and achieve stable and smooth control results with difficulty.To address this problem,we propose a data-efficient hierarchical car following control method that does not depend on car kinematic models.The upper layer of the proposed method constructs a dataset based on the perception results of car coordinates,speed,and other onboard sensors.A deep reinforcement learning model is trained to perform car following,avoiding the reliance on prior knowledge and eliminating the need for real-world training.Training samples are randomly selected from the dataset,which improves data utilization.The lower layer of the method implements real-time control of the car acceleration and angular velocity using a PID controller,which avoids the control jitter caused by the instability of deep reinforcement learning policies,resulting in smoother control.To verify the performance of the algorithm,both simulation and real-world experiments are conducted.Experimental results show that the proposed algorithm can keep the distance between the following car and the target car within a safe and reasonable range.The comparative experiments demonstrate that the proposed algorithm achieves more stable,smoother,and safer car following control in both lateral and longitudinal directions.
With the increasing diversification of network functions,the software-defined networking(SDN) architecture,which provides centralized network control and programmability,has been deployed in various fields.However,the unique hierarchical structure and operation mechanism of SDN also introduce new security challenges,among which as the carrier of control plane management decisions and the basis of data plane network behavior,flow rules have become the focus of SDN attack and defense.Aiming at the security issues of flow rules in SDN,this paper first reviews the characteristics and security risks of the SDN architecture.Based on the mechanism of flow rules in SDN,the attacks against flow rules are systematically divided into two categories,namely,interference of control plane decision and violation in data plane implementation,with the attack examples introduced.Then,the methods for improving the security of flow rules are analyzed and classified into two categories,i.e.,checking and enhancing the security of flow rules.Furthermore,existing implementation mechanisms are summarized with their limitations briefly analyzed.In terms of flow rule security checking,two mainstream methods,i.e.,model-based checking and test-packet-based checking,are analyzed and discussed.In terms of flow rule security enhancement,three specific ideas based on permission control,conflict resolution and path verification are introduced and discussed.Finally,the future research trends of flow rule security are prospected.
In the scenario of data outsourcing,access control and key update have an important application value.However,it is hard for existing encrypted deduplication schemes to provide flexible and effective access control and key update for outsourcing user data.To solve this problem,an encrypted deduplication scheme with access control and key updates is proposed.First,an efficient access control scheme for encrypted deduplication is designed based on the ciphertext-policy attribute-based encryption and the proof of ownership.It combines access control with proof of ownership and can simultaneously detect whether a client has the correct access right and whole data content only through a round of interaction between the client and the cloud server,effectively preventing unauthorized access and ownership fraud attacks launched by adversaries.The scheme has features such as low computation overhead and few communication rounds.Second,by combining the design ideas of server-aided encryption and random convergent encryption,an updatable encryption scheme suitable for encrypted deduplication is designed.It is combined with the proposed access control scheme to achieve hierarchical and user-transparent key updates.The results of security analysis and performance evaluation show that the proposed scheme can provide confidentiality and integrity for outsourcing user data while achieving efficient data encryption,decryption,and key update.
Some important documents such as contracts,certificates and notifications are often stored and disseminated in a digital format.However,due to the inclusion of key text information,such images are often easily illegally tampered with and used,causing serious social impact and harm.Meanwhile,taking personal privacy and security into account,people also tend to remove sensitive information from these digital documents.Malicious tampering and desensitization can both introduce extra traces to the original images,but there are differences in motivation and operations.Therefore,it is necessary to differentiate them to locate the tamper areas more accurately.To address this issue,we propose a convolutional encoder-decoder network,which has multi-level features of the encoder through U-Net connection,effectively learning tampering and desensitization traces.At the same time,several Squeeze-and-Excitation attention mechanism modules are introduced in the decoder to suppress image content and focus on weaker operation traces,to improve the detection ability of the network.To effectively assist network training,we build a document image forensics dataset containing common tampering and desensitization operations.Experimental results show that our model performs effectively both on this dataset and on the public tamper datasets,and outperforms comparison algorithms.At the same time,the proposed method is robust to several common post-processing operations.
At present,the existing histogram publishing algorithms based on centralized or local differential privacy for graph data degree distribution can neither balance the privacy and utility of published data,nor preserve the identity privacy of end users.To solve this problem,a histogram publishing algorithm for degree distribution via shuffled differential privacy(SDP) is proposed under the framework of Encode-Shuffle-Analyze.First,a privacy preserving framework for histogram publishing of degree distribution is designed based on shuffled differential privacy.In this framework,the noisy impact that the encoder brings to distributed users is reduced by employing interactive user grouping,the shuffler and the square wave noise mechanism,while adding noise via local differential privacy.The noisy histogram of degree distribution is reconciled via the maximum likelihood estimation at the analyzer end,thus improving the utility of published data.Second,specific algorithms are proposed for concreting distributed user grouping,adding shuffled differential privacy noise and reconciling the noisy data,respectively.Furthermore,it is proved that these algorithms meet the requirement of(ε,σ)-SDP.Experiments and comparisons illustrate that the proposed algorithms can preserve the privacy of distributed users,and that the data utility is improved more than 26% with metrics in terms of L1 distance,H distance and MSE in comparison with the existing related algorithms.The proposed algorithms also perform with a low overhead and stable data utility,and are suitable for publishing and sharing the histogram of degree distribution for different scales of graph data.
Boolean functions have important applications in cryptography.Bent functions have been a hot research topic in symmetric cryptography as Boolean functions have maximum nonlinearity.From the perspective of spectrum,bent functions have a flat spectrum under the Walsh-Hadamard transform.Negabent functions are a class of generalized bent functions,which have a uniform spectrum under the nega-Hadamard transform.A generalized negabent function is a function with a uniform spectrum under the generalized nega-Hadamard transform.Bent functions has been extensively studied since its introduction in 1976.However,there are few research on negabent functions and generalized negabent functions.In this paper,the properties of generalized negabent functions and generalized bent-negabent functions are analyzed.Several classes of generalized negabent functions,generalized bent-negabent functions,and generalized semibent-negabent functions are constructed.First,by analyzing a link between the nega-crosscorrelation of generalized Boolean function and the generalized nega-Hadamard transformation,a criterion for generalized negabent functions is presented.Based on this criterion,a class of generalized negabent functions is constructed.Secondly,two classes of generalized negabent functions of the form f(x)=c1f1(x(1))+c2f2(x(2))+…+crfr(x(r)) are constructed by using the direct sum construction.Finally,generalized bent-negabent functions and generalized semibent-negabent functions over Z8 are obtained by using the direct sum construction.Some new methods for constructing generalized negabent functions are given in this paper,which will enrich the results of negabent functions.