With the exponential increase in information security risks, ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment. However, experts possess a limited understanding of fundamental security elements, such as assets, threats, and vulnerabilities, due to the confidentiality of airborne networks, resulting in cognitive uncertainty. Therefore, the Pythagorean fuzzy Analytic Hierarchy Process (AHP) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks. First, Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights, which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks. Second, Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure, which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios. Finally, a comparative analysis was conducted. The proposed method demonstrated the highest Kendall coordination coefficient of 0.952. This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.
In order to solve the problems of existing fingerprint localization technology,such as large amount of fingerprint data,difficulty in storage and processing,and insufficient adaptability to positioning in complex spaces,a three-dimensional indoor space fingerprint localization solution based on massive multiple-input multiple-output(MIMO)system is proposed.First,an Angle Delay Channel Frequency Power(ADCFP)fingerprint matrix with faster processing speed and smaller storage requirements is proposed.Secondly,a new similarity criterion,namely chi-square distance,is introduced to improve the positioning accuracy,and then an improved power Weighted K-Nearest Neighbor(WKNN)matching algorithm is proposed.The impact of the power value on the weight reduction speed is different,and different weights are allocated according to the fingerprint similarity.Finally,three types of compressed fingerprints are obtained by using row-by-column compression of ADCFP,further reducing the amount of fingerprint data.And the Central Angle of Arrival(CAOA)Clustering Algorithm is introduced to shorten the positioning time.The simulation results show that the ADCFP fingerprint matrix can offer a 89.2% reliability for 2 m accuracy.The average positioning error using chi-square distance is reduced by 5.63% compared with that using the Manhattan distance.The improved power WKNN algorithm reduces the average localization error by 4.45% compared with the traditional WKNN algorithm.The introduction of CAOA Clustering Algorithm can increase the localization speed to 1.72 times that of the non-clustering case.The average localization error is reduced by 44.05% compared with the K-means Clustering Algorithm,and the positioning performance is greatly improved.
With regard to the covert communication system based on transform domain technique,the anti-interception performance of weighted fractional Fourier transform(WFRFT)communication systems is discussed.Theoretical analysis and simulation results illustrate that the communication system based on WFRFT has actual performance boundary of anti-detection and anti-recognition in which the covert performance remains unchanged as the number of weights is more than 4.In accordance with this problem,a cross-layer and parallel WFRFT covert communication system is proposed.Firstly,a parallel multi-channel WFRFT processing architecture is adopted to effectively increase the number of transformation parameters.Secondly,based on the tack of cross-layer design,the parameters of each WFRFT are controlled by the destination address which can further increase the difficulty in intercepting and recognizing signal for the non-cooperative receivers.The simulation results show that the cross-layer parallel WFRFT covert communication system is superior to the multi-parameter WFRFT covert communication system in terms of anti-detection and anti-modulation recognition.
In response to the problem of low accuracy in automatic modulation recognition under low signal-to-noise ratio(SNR)conditions,the authors propose a channel gated residual 2-network(Res2Net)convolutional neural network(CNN)model.The model mainly consists of two-dimensional CNN(2D-CNN),multi-scale Res2Net,squeeze-and-excitation network(SENet)and long short-term memory(LSTM)network,which extracts multi-scale features from raw I/Q data through convolution,adjusts the weight of feature channels through gating mechanism,and uses LSTM to model the sequence of convolutional features to ensure effective data feature mining,thereby improving the accuracy of automatic modulation recognition.The modulation recognition experiment on the benchmark dataset RML2016.10a shows that the recognition accuracy of the proposed model is 92.68%at 12 dB SNR,and the average recognition accuracy is above 91% when the SNR is greater than 2 dB.Compared with classical CLDNN model,LSTM model,similar PET-CGDNN model and CGDNet model,the proposed model can achieve higher modulation type recognition accuracy.
For the information security problem of energy reveiver as potential eavesdropper in multi-antenna simultaneous wireless information and power transfer(SWIPT)system,a physical layer security transmission scheme assisted by intelligent reflecting surface(IRS)and artificial noise is proposed.Firstly,considering the transmit power,energy harvesting threshold and unit module constraints of IRS,a nonlinear and multivariable coupling nonconvex problem is established.The beamforming matrix,artificial noise covariance matrix and phase shift matrix at IRS are jointly optimized to maximize the system secrecy rate.Then the nonconvex objective function is transformed equivalently based on mean square error criterion and the nonconvex energy harvesting constraints are treated by Successive Convex Approximation(SCA)method.Finally,the transmitter variables and IRS variables are obtained by Lagrangian dual method and the Majorization-Minimization(MM)algorithm based on price mechanism respectively.Simulation results show that the proposed algorithm can significantly increase the secrecy performance and guarantee the energy harvesting requirement as compared with existing schemes.
Ground-based pseudolites systems usually use time hopping/direct sequence-code division multiple access(TH/DS-CDMA)signals for pseudo-range measurement and positioning in order to overcome near-far effect,which leads to the design of the receiving terminal must increase the phase acquisition of system TH sequences,reducing the capture efficiency and introducing the capture complexity of the receiving terminal.To address above problems,the authors propose a joint acquisition algorithm based on the pseudolites TH signals.Using the time-domain orthogonal characteristics of the TH signal and time slot allocation characteristics,the acquisition results of multi-station single-pulse signals are mapped to enable time slot sequences,and the acquisition of TH parameters is completed by circular truncation correlation,which effectively reduces the TH parameter estimation time.The simulation test results show that the method can correctly complete the parameter estimation in the acquisition phase,and the acquisition time of four pseudo-satellites combined is reduced by 63.32% compared with the traditional method.It provides an effective method for fast acquiring of pseudo-satellite TH signals.
The problem of direct sequence spreading spectrum(DSSS)signal sensing under non-cooperative conditions has always been a hot research topic in the field of communication countermeasures,and the performance of traditional sensing methods is seriously degraded under low signal to noise ratio(SNR)conditions.In order to effectively improve the performance of DSSS signal sensing,a DSSS signal sensing method based on an improved residual neural network(ResNet)is proposed.Firstly,the correlation peak characteristics of the DSSS signal are quickly extracted by the Generalized Cross Correlation(GCC)algorithm.Then,based on the ResNet,the attention mechanism is introduced to build a network model to analyze and identify the abstract features.Finally,experimental validation is carried out in the simulated dataset.The results show that,compared with the time-domain autocorrelation method and the convolutional neural network method,the proposed method has a better sensing effect and can effectively sense the DSSS signals with-16 dB SNR.
For the problem that the scheduling algorithm of signal processing application in the current heterogeneous signal processing platform has a single optimization goal and the processor load is unbalanced in the scheduling results,a load balancing algorithm based on Ant Colony Optimization(ACO)algorithm is proposed.The algorithm combines the fast search ability and combinatorial optimization ability of ACO algorithm,takes the scheduling length of signal processing application and processor load balance as the optimization goal,improves the initial pheromone matrix and ant traversal order,proposes scheduling length heuristic factor and load balance heuristic factor to improve the processor selection formula,and uses Roulette strategy to determine the processor assigned to each subtask of signal processing application,thus completing the scheduling of signal processing applications.The simulation results show that the scheduling results obtained by the algorithm are improved in terms of scheduling length and load balancing.It can give full play to the performance of each processor and improve the overall efficiency of heterogeneous signal processing platform.
In a phased array TT&C system,due to the coupling effect of angle error on range rate measurement and ranging errors,the errors of range rate measurement and ranging are increased.To analyze these special errors,the author proposes Additional Error Analysis Method and Reference Array Element Method.The reference elements in phased array,equivalent to mechanical scanning antennas,are used as comparison benchmark,thus obtaining the additional error increased by the phased array of previous machine scanning antenna TT&C systems through analysis.The total error is the sum of this additional error and the error of the original machine scanning antenna TT&C system.The methods inherit the mature theory of range rate and range measurement error analysis of previous machine scanning antenna TT&C systems,focusing on the special problems after phased array.The physical concept of the methods is relatively clear,and they have great engineering practical significance.
In order to obtain high-definition time-frequency map and high-precision parameter estimation of frequency hopping(FH)signals,an FH signal parameter estimation method based on autocorrelation and time-frequency analysis method is proposed.Firstly,the segmented autocorrelation algorithm based on energy detection is used to preprocess the receiving signal,and then the time-frequency transformation is carried out to obtain the time-frequency matrix of the signal.The signals are extracted by binarization and morphological filtering,and the parameters are estimated by clustering algorithm.Simulation results show that under the condition of low signal-to-noise ratio(SNR),this method can obtain high-definition time-frequency images and high-precision parameter estimates.When the lowest SNR is-11 dB,the order of magnitude of the estimation error is 10-7.Meanwhile,the autocorrelation operation can improve the anti-noise performance of the parameter estimation algorithm obviously.
Using radar to simultaneously monitor human respiration and heartbeat has the advantages of non-contact and high privacy,so it has become one of the research focuses at home and abroad.The working principles of different types of radar system are analyzed for the simultaneous monitoring of human respiration and heartbeat.The methods of radar data preprocessing,respiratory and heartbeat signal separation,and respiratory rate and heart rate estimation in human signal detection are summarized.Finally,the trends of detecting human vital signal based on the radar system are pointed out.
The spatial and temporal distribution of current heterogeneous network spectrum environment is complex and variable,the data preprocessing of existing multi-user cooperative sensing methods is cumbersome,and the sensing efficiency is low.For above problems,a cooperative learning-based spectrum intelligent sensing algorithm is proposed under a system architecture consisting of user sensing layer and edge fusion layer.The user-aware layer uses a multi-branch convolutional recurrent gated neural network to realize local sensing by using the underlying structural information of the original normalized energy signal.The edge fusion layer performs message propagation based on a self-attention mechanism and fuses the sensing results of each unauthorized user in the user-aware layer to arrive at the final decision.Experiments show that when the signal-to-noise ratio is-20 dB and five users are sensing cooperatively,the proposed method is able to achieve a detection probability of 18.3% at a false alarm probability of 1.91% ,an improvement of 6.1% compared with the comparison model,and does not require additional pre-processing of the raw data,thus reducing the complexity of the algorithm.
To satisfy the future demand for multi-target tracking in hemispherical coverage,a 32-element circular polarized semi-spherical conformal phased array antenna is present in this paper.Hemispherical and cylinder configuration is adopted to meet the communication requirements at low elevation angel.Activate corner domain and different element distributions are compared in detail to decide the optimum performance.Simulated results show that the gain is higher than 13.8 dBi and the gain flatness is less than 1.8 dB within the scanning range of 0°～90°,which indicates this spherical phased array antenna can be applied in the oc-casions of multi-target tracking in hemispherical coverage.
As an important model in 5G,the Fog Radio Access Network(F-RAN)achieves significant performance gains through technique among device-to-device(D2D)communications and wireless relays.Especially,caching appropriately in the edge devices allows content caching users(CUs)to send content to content requesting users(RUs)directly,which effectively reduces the burden of the fronthaul and download delay.The authors consider a scenario where users send content requests and get delivered under the F-RAN model.By modeling the content request queue at each CU as an independent M/D/1 queue model,the authors analyze and derive the cache hit probability of CUs and average download delay expressions under content caching and delivery policies.It is proved that the relationship between the cache hit probability of CUs and content frequency distribution can help realizing the approximate optimal solution of the former.On this basis,the authors establish an optimization problem under the expectation perspective over some time and propose a frequency distribution(FD)-based algorithm with delivery control when implementing to solve it.The simulation results show that compared with the existing caching policies,the policy optimizing the FD of all content can maximize the cache hit probability of CUs and reduce the average download delay.
In order to solve the problem of offloading decision for computation-intensive tasks in dependency-aware edge networks,a depth-first search scheduling strategy based on task priority is proposed.By taking into account the limited energy and high mobility of users,the network model of joint downlink energy harvesting and uplink computing task offloading is built.Furthermore,the device-to-device optimization objective function is formulated under the constraints of the latency and the task priority and the task offloading problem is modeled as a Markov decision process.By exploiting the advantage of self-learning of deep reinforcement learning,the Dueling Double DQN(D3QN)algorithm based on task dependency is designed to tackle it.Numerical results show that the proposed method can meet the delay requirements of more users and reduce the completion delay up to 9% ～10% against other existing schemes.
Radiation source location is an important basis for military operations on the battlefield.However,in the complex battlefield electromagnetic environment,it is difficult to establish an effective propagation model,and under the condition of multiple radiation sources,it is difficult for traditional positioning methods to realize locating quickly and accurately.In order to solve above problems,based on the distributed electromagnetic situation awareness network of ground movable unmanned platform,a radiation source localization method is proposed to intelligently optimize the node sensing position,and the improved Cuckoo Search Algorithm is used to optimize the node sensing position in the network,and then the Kriging Interpolation is used to realize the perception of electromagnetic situation,and K-means clustering of situation data combined with Density Peaks Clustering(DPC)ideas to achieve radiation source localization.This method can adjust the sensing position under the condition of unsatisfactory initial deployment to achieve target radiation localization without prior information.Experiments show that the interpolation accuracy of the proposed method is better than that of Kriging Interpolation of random deployment and ordinary Cuckoo Optimized Sensing position in a multi-target complex environment,and the average positioning error is 47.28 m while the perception area is 4 km×4 km,the total number of nodes is 40 000,and the perception node accounts for 1%,so it has certain application prospects.
A scheduling-based optimization strategy for small satellite routing is proposed in order to solve the problems about frequent link switches and low resource utilization in the small satellite communication.A small satellite constellation model is established and then a satellite routing is optimized according to the moving direction(approximately perpendicular to the equatorial plane)and the real-time connection policy regarding the location dimension of satellites.Facing the contradiction between the ever increase of network services and the limited onboard resources,the service data packets are classified into real-time data packets and non-real-time data packets.A weighted round-robin scheduling is implemented to ensure its service quality and resource utilization.The simulation results show that,compared with the existing small satellite routing algorithms,the optimized routing algorithm can effectively reduce the data transmission delay and increase the system throughput.
The wideband frequency hopping(FH)communication signal's carrier FH will result in a fluctuating Doppler frequency shift in the case of shifting multi-station location.As a result,the Doppler frequency difference cannot be estimated directly by using multiple different pulses of the FH signal.So the precision is poor and the positioning requirements cannot be met if the time-frequency difference of a single FH pulse is estimated alone.A method based on the coherent accumulation of cross ambiguity function for calculating the time difference and Doppler velocity difference of wideband FH signals is suggested to address this problem.In the first place,the Doppler effect difference of the wideband FH signal is represented as the Doppler velocity difference,eliminating the necessity to estimate the Doppler frequency difference and the impact of carrier FH on the estimation of the Doppler effect difference.Additionally,the time-varying velocity-difference and time-varying time-difference signal models are developed.The cross ambiguity function of each single-hop signal is then phase-compensated to achieve phase alignment.The coherence estimation results of the initial time difference and velocity difference of the multi-FH pulse signal can be obtained by conducting the two-dimensional peak search of the time difference and velocity difference on the cross ambiguity function after coherent accumulation.Finally,with Link16 data link signal as an example,the correctness and accuracy of the algorithm is proved by simulation experiment.
The dependence on wireless channel and the openness of wireless channel make unmanned aerial vehicle(UAV)vulnerable to malicious electromagnetic jammings.To combat channel-following jamming from jammers,a reinforcement learning-based anti-jamming strategy is proposed based on the perception of jamming spectrum information.The power control and channel access strategy of UAV is modeled as Markov Decision Process(MDP),and the anti-jamming strategy of the communication system is intelligently optimized by Reinforcement Learning Algorithm.An anti-jamming algorithm based on Win or Learn Fast Policy Hill-climbing(WoLF-PHC)algorithm is proposed.The simulation results prove that the proposed algorithm can reduce the user's Interference-to-Signal Ratio(ISR)less than 0.1,and increase the user's achievable rate by 14%on the basis of the initial value.Compared with Q-learning Algorithm and PHC Algorithm,it has better anti-interference transmission performance.
