Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.

Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Design and Implementation of Network Communication Interface Based on μCLinux

In order to meet the requirements of real-time and high efficient data transmission in data acquisition and industrial control fields,a scheme of network communication interface is presented. The design of hardware circuit and the realization of software program are mainly introduced. The result of the experiment shows that this embedded system has high reliability and stability. It can realize high-speed data transmission,and also can satisfy the requirements of network communication in the industrial control fields.

New Individual Identification Method of Radiation Source Signal Based on Entropy Feature and SVM

In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firstly,based on the theory of multi-resolution wavelet analysis,the wavelet power spectrum of noncooperative signal can be gotten. Secondly,according to the information entropy theory,the wavelet power spectrum entropy is defined in this paper. Therefore,the database of signal's wavelet power spectrum entropy can be built in different SNR and signal parameters. Finally,the sorting and identification model based on SVM is built for the individual identification of radiation source signal. The simulation result indicates that this method has a high individual's identification rate in low SNR,when the SNR is greater than 4 dB,the identification rate can reach 100%. Under unstable SNR conditions,when the range of SNR is between 0 dB and 24 dB,the average identification rate is more than 92. 67%. Therefore,this method has a great application value in the complex electromagnetic environment.

Joint Trajectory and Passive Beamforming Optimization in IRS-UAV Enhanced Anti-Jamming Communication Networks

This paper investigates the anti-jamming communication scenario where an intelligent reflecting surface(IRS)is mounted on the unmanned aerial vehicle(UAV)to resist the malicious jamming attacks.Different from existing works,we consider the dynamic deployment of IRS-UAV in the environment of the mobile user and unknown jammer.Therefore,a joint trajectory and passive beamforming optimization approach is proposed in the IRS-UAV enhanced networks.In detail,the optimization problem is firstly formulated into a Markov decision process(MDP).Then,a dueling double deep Q networks multi-step learning algorithm is proposed to tackle the complex and coupling decision-making problem.Finally,simulation results show that the proposed scheme can significantly improve the anti-jamming communication performance of the mobile user.

Modulation recognition of communication signals based on SCHKS-SSVM

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

High Frequency Communication Network with Diversity: System Structure and Key Enabling Techniques

High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication network(HFCN) in this paper.First, we present the benefits and the challenges by introducing diversity technology into the existing HFCN. Secondly, to exploit the benefits fully and overcome the challenges, we propose a system structure suitable for deploying diversity technology in HFCN in large scale,based on the cloud radio access network and software defined network. Moreover, we present a general structure for the real-time updating frequency management system that plays a more important role especially when resource consuming(e.g., frequency) diversity technology is deployed. Thirdly, we investigate the key techniques enabling diversity technology deployment. Finally, we point out the future research directions to help the HFCN with diversity work more efficiently and intelligently.

Direction finding of coexisted independent and coherent signals using electromagnetic vector sensor

The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.

Design of task priority model and algorithm for imaging observation problem

In the imaging observation system, imaging task scheduling is an important topic. Most scholars study the imaging task scheduling from the perspective of static priority, and only a few from the perspective of dynamic priority. However,the priority of the imaging task is dynamic in actual engineering. To supplement the research on imaging observation, this paper proposes the task priority model, dynamic scheduling strategy and Heuristic algorithm. At first, this paper analyzes the relevant theoretical basis of imaging observation, decomposes the task priority into four parts, including target priority, imaging task priority, track, telemetry & control(TT&C)requirement priority and data transmission requirement priority, summarizes the attribute factors that affect the above four types of priority in detail, and designs the corresponding priority model. Then, this paper takes the emergency tasks scheduling problem as the background, proposes the dynamic scheduling strategy and heuristic algorithm. Finally, the task priority model,dynamic scheduling strategy and heuristic algorithm are verified by experiments.

Design and multistability analysis of five-value memristor-based chaotic system with hidden attractors

A five-value memristor model is proposed,it is proved that the model has a typical hysteresis loop by analyzing the relationship between voltage and current.Then,based on the classical Liu-Chen system,a new memristor-based fourdimensional(4D)chaotic system is designed by using the five-value memristor.The trajectory phase diagram,Poincare mapping,bifurcation diagram,and Lyapunov exponent spectrum are drawn by numerical simulation.It is found that,in addition to the general chaos characteristics,the system has some special phenomena,such as hidden homogenous multistabilities,hidden heterogeneous multistabilities,and hidden super-multistabilities.Finally,according to the dimensionless equation of the system,the circuit model of the system is built and simulated.The results are consistent with the numerical simulation results,which proves the physical realizability of the five-value memristor-based chaotic system proposed in this paper.

New Three-Dimensional Assessment Model and Optimization of Acoustic Positioning System

This paper addresses the problem of assessing and optimizing the acoustic positioning system for underwater target localization with range measurement.We present a new three-dimensional assessment model to evaluate the optimal geometric beacon formation whether meets user requirements.For mathematical tractability,it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance,which is distance-dependent.Then,the relationship between DOP parameters and positioning accuracy can be derived by adopting dilution of precision(DOP)parameters in the assessment model.In addition,the optimal geometric beacon formation yielding the best performance can be achieved via minimizing the values of geometric dilution of precision(GDOP)in the case where the target position is known and fixed.Next,in order to ensure that the estimated positioning accuracy on the region of interest satisfies the precision required by the user,geometric positioning accuracy(GPA),horizontal positioning accuracy(HPA)and vertical positioning accuracy(VPA)are utilized to assess the optimal geometric beacon formation.Simulation examples are designed to illustrate the exactness of the conclusion.Unlike other work that only uses GDOP to optimize the formation and cannot assess the performance of the specified size,this new three-dimensional assessment model can evaluate the optimal geometric beacon formation for each dimension of any point in three-dimensional space,which can provide guidance to optimize the performance of each specified dimension.

Design and Implementation of Fingerprint Identification System Based on KNN Neural Network

Fingerprint identification and recognition are considered popular technique in many security and law enforcement applications. The aim of this paper is to present a proposed authentication system based on fingerprint as biometric type, which is capable of recognizing persons with high level of confidence and minimum error rate. The designed system is implemented using Matlab 2015b and tested on a set of fingerprint images gathered from 90 different persons with 8 samples for each using Futronic’s FS80 USB2.0 Fingerprint Scanner and the ftrScanApiEx.exe program. An efficient image enhancement algorithm is used to improve the clarity (contrast) of the ridge structures in a fingerprint. After that core point and candidate core points are extracted for each Fingerprint image and feature vector have been extracted for each point using filterbank_based algorithm. Also, for the matching the KNN neural network was used. In addition, the matching results were calculated and compared to other papers using some performance evaluation factors. A threshold has been proposed and used to provide the rejection for the fingerprint images that does not belong to the database and the experimental results show that the KNN technique have a recognition rate equal to 93.9683% in a threshold equal to 70%.

Study on the Characteristics of LFM Signals, BC Signals and Their Mixed Modulation Signals

This paper proposes a linear frequency modulation (LFM signal) and biphase coding (BC signal) mixed modulation signal called LFM-BC signal. LFM-BC signal has both LFM signal and BC signal two kinds of traditional signal advantages but makes up for their shortcomings. In this paper, LFM-BC signal, LFM and BC signals are studied and compared from the time characteristic and frequency characteristic of the signal, fuzzy function, pulse compression and Doppler characteristics and low probability of interception (LPI) characteristics.

Novel joint encoding/decoding algorithms of fountain codes for underwater acoustic communication

Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform（LT） codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage（IC） of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced,high coding overhead is required and decoding delay is increased.These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First,a doping and non-uniform selecting（DNS） encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation（OFG-BP） decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.

Time-Frequency Bandwidth Product Estimation of Sinusoidal Non-Linear Chirp Keying Scheme

Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.

Fault Detection and Recovery for Full Range of Hydrogen Sensor Based on Relevance Vector Machine

In order to improve the reliability of hydrogen sensor,a novel strategy for full range of hydrogen sensor fault detection and recovery is proposed in this paper. Three kinds of sensors are integrated to realize the measurement for full range of hydrogen concentration based on relevance vector machine( RVM). Failure detection of hydrogen sensor is carried out by using the variance detection method. When a sensor fault is detected,the other fault-free sensors can recover the fault data in real-time by using RVM predictor accounting for the relevance of sensor data. Analysis,together with both simulated and experimental results,a full-range hydrogen detection and hydrogen sensor self-validating experiment is presented to demonstrate that the proposed strategy is superior at accuracy and runtime compared with the conventional methods. Results show that the proposed methodology provides a better solution to the full range of hydrogen detection and the reliability improvement of hydrogen sensor.

Dynamic Investigations on the Wear Behavior of a 3D Revolute Joint Considering Time-Varying Contact Stiffness: Simulation and Experiment

The existence of the relative radial and axial movements of a revolute joint’s journal and bearing is widely known.The three-dimensional(3D)revolute joint model considers relative radial and axial clearances;therefore,the freedoms of motion and contact scenarios are more realistic than those of the two-dimensional model.This paper proposes a wear model that integrates the modeling of a 3D revolute clearance joint and the contact force and wear depth calculations.Time-varying contact stiffness is first considered in the contact force model.Also,a cycle-update wear depth calculation strategy is presented.A digital image correlation(DIC)non-contact measurement and a cylindricity test are conducted.The measurement results are compared with the numerical simulation,and the proposed model’s correctness and the wear depth calculation strategy are verified.The results show that the wear amount distribution on the bearing’s inner surface is uneven in the axial and radial directions due to the journal’s stochastic oscillations.The maximum wear depth locates where at the bearing’s edges the motion direction of the follower shifts.These find-ings help to seek the revolute joints’wear-prone parts and enhance their durability and reliability through improved design.

Prediction and Analysis of Society’s Networks

According to the information theory model, I use the qualitative analysis to explain the information flows through the media channel become news. Besides, I make use of a pyramid model to expound the relationship among each media. I combine the information theory model into the prediction process, taking advantage of curve fitting and listing out different ratios of media capacities. Finally I get the small relative error between the fitting result and the reality. Based on the ELM model, I classify the information in two ways on the basis of the four specific factors. By taking the influence degree of public opinion into account, I quantify the factors with different ratios to determine how they could be used in information spreading.

A Demand Analysis and Teaching Design of English for Specific Military Purposes for Non-commissioned Officers

Based on the theory of“demand analysis”,this paper investigates and analyzes the motivation,and demand of non-commissioned officers for ESMP(English for Specific Military Purposes)by means of questionnaire and interview,in an attempt to construct the framework of ESMP teaching design for non-commissioned officers.The study points out that the design of ESMP for non-commissioned officers should be oriented by“demand analysis”theory,explore the effective integration of professional knowledge and language acquisition,and promote the cultivation of non-commissioned officers’practical ability of professional English application.

Simulation Analysis of Multiple BP Decoding Algorithm and FFT-BP Decoding Algorithm

In order to solve the problem that the original decoding algorithm of multi-band LDPC codes is high and is not conducive to hardware implementation, two simplified decoding algorithms for multi-band LDPC codes are studied: the reliability propagation based on fast Fourier transform Code algorithm (FFT-BP) and log-BP decoding algorithm based on logarithmic operations. The simulation results show that the FFT-BP decoding algorithm is more convenient and efficient.