This paper tries to improve the attitude control ability of the unmanned helicopter.The fal function is not smooth at the inflection pointwhich may cause system chatteringand the system gain is large when the error is large.To solve the problemsan improved fal function is proposed to rewrite the nonlinear error feedback lawand it is re-applied to Active Disturbance Rejection Control (ADRC) to obtain an improved ADRC.Based on the established dynamics model of an unmanned helicopterthe attitude angle tracking and anti-jamming simulations under white noise and step interference are conducted on Matlab/Simulink.The simulation results show that: 1) The improved fal function is smoother at the inflection point; and 2) The ADRC based on the improved fal function still has good response speed and low overshoot in the attitude angle tracking of the unmanned helicopterand its anti-jamming ability is better than that of the traditional ADRC.

In view of the wind gusts and unmodeling characteristics in trajectory tracking control of a quadrotor aircrafta new method is presented.This controller is called dual-loop conditional integral sliding mode controllerwhich is based on nonlinear sliding mode control techniqueconditional integral idea and Lyapunov stability theory.The nonlinear dynamic model of the quadrotor is linearized to simplify the mathematical model of the controlled object and improve the efficiency of controller design.The trajectory tracking controllers of position loop and attitude loop are designed by using the conditional integral sliding mode control lawand global asymptotic stability of the control system is realized.The effectiveness of the designed controller is verified by simulation cases.The simulation results show that the controller has higher control accuracystronger anti-interference ability and stronger robustness compared with other controllers.It can effectively suppress system chatteringeliminate static errors and realize high-performance trajectory tracking of a quadrotor aircraft.

In order to find out the influence of various factors on the operational application of high-repetition-frequency laserthe principle of high-repetition-frequency laser jamming on semi-active guided missile is studiedand the influence of different factorssuch as atmospheric transmissi onrepeated frequency of jamming laser and configuration distance on the operational application of high-repetition-frequency laser is discussed respectively. The corresponding mathematical models are establishedand the simulation and calculation are conducted. The following three conclusions can be drawn from the results. 1) The higher the atmospheric visibilitythe higher the minimum peak power required by the high-repetition-frequency laser jammer; 2) The higher the jamming repetition frequencythe higher the probability of jamming success rate; and 3) The jammers shall not be configured too close or too far awayand the configuration range of the jammers should be determined according to the performance of the semi-active guided missile and the probability of jamming lead.

This paper focuses on large-angle trajectory tracking control of a quadrotor in discrete time domain.Firstlythe Newton-Euler method is used to establish a dynamic model based on 3D rotation group SO(3)which is then transformed into a discrete-time model by using the forward Euler method.Nexta control scheme based on the structure of inner and outer loops is proposed.Thendiscrete-time sliding mode control is applied to the design of inner and outer loop controllerswhose stability is proved by Lyapunov function.Finallythe performance of the designed control system is verified by Matlab simulation.The simulation results show that the designed control system has good large-angle trajectory tracking performance.

Aiming at the modeling and unmodeling uncertainty of the photoelectric stabilized platforman Adaptive Robust Control (ARC) algorithm based on disturbance estimation is proposed.The algorithm combines ARC with the Disturbance Observer (DOB)and integrates the disturbance estimation results obtained by DOB into the design of ARC algorithm.The adaptive control law of ARC is used for adaptive compensationso as to further improve the stabilized accuracy of the photoelectric stabilized platform.The simulation results show that:Compared with the traditional DOB feedforward compensationthe proposed algorithm has the advantages of smaller overshoot and shorter adjustment timewhich effectively improves the disturbance suppression ability of the photoelectric stabilized platform.

The initial value of the two-step method will seriously affect the accuracy of star sensors optical error calibration algorithm.To solve the probleman optimization method of the two-step method based on Artificial Fish Swarm Algorithm (AFSA) is proposed.This method takes advantage of the characteristics of AFSAwhich can easily jump out of local optimum and then conduct global optimization.Before the implementation of the two-step methodthe output of a group of optimal initial values is used for the initialization of the two-step method,and finally the calibration values with higher accuracy are obtained.The simulation results show that the improved two-step method can improve the calibration accuracy and stability by using the advantages of AFSA as well as overcoming the disadvantages of the two-step method.

To solve the problem of low tracking accuracy when fusing different sensorsan Improved Adaptive Unscented Kalman Filter (IAUKF) algorithm using weighted data fusion is proposed.In the processing of fusing different sensorsscene switching will cause decline in sensor accuracy.Through introducing the idea of Sage-Husa adaptive filteringdifferent weights are set for data from different sensorsand the statistical characteristics of measurement noise are processed in real time.Joint Probabilistic Data Association (JPDA) is used to remove clutter and associate measurement with target trajectories.This algorithm is used to track multiple aerial targets in the modified spherical coordinate system.The simulation results show that the new algorithm effectively reduces state estimation errors and improves tracking accuracy in comparison with the corresponding method based on the standard UKF algorithm.

The existing DOA estimation methods based on Nested Sparse Circular Arrays (NSCA) suffer from high computational complexity and the difficulty in fast selecting of super parameters.To solve the problemsan Off-Grid Sparse Bayesian Learning (OGSBL) method based on the improved NSCA is proposed.The covariance matrix of the received signals of the improved NSCA is vectorizedand an extended observation matrix is constructed.Thenthe under-determined DOA estimation is realized by using the off-grid model and the Sparse Bayesian Learning (SBL) algorithm.The simulation results show that the proposed algorithm reduces computational complexitythe super parameters of the model can be adjusted adaptivelyand the performance of the proposed algorithm is better than that of the DOA estimation algorithms based on the original NSCA and the traditional uniform circular arrays under the conditions of low SNRsmall snapshots and multiple sources.

In order to support air-to-surface multi-target attacksthe model of air-to-surface multi-target common release zone is studiedand the definition of common release zone is given.A solution of multi-target common release zone based on polygon intersection is given.The longest attacking route of the common release zone and the entry point are modeled.Furthermorethe method of batch division is proposed to support the whole process of attacking.The method is a deterministic algorithm with determined computational complexity and high precisionwhich is suitable for airborne real-time calculation environment.Finallythe model of air-to-surface multi-target attacks is simulated and implementedoutputting the common release zone and batch division results.The simulation results show that the model of multi-target attacking is correct and effectivewhich can realize batch division and attacking route planning in air-to-surface multi-target attacks.

The problem of robust control for a class of Lipschitz nonlinear systems with disturbances is studiedand the problem of observer-based robust control for nonlinear systems is discussed.Firstlya state observer is constructed for the nonlinear system satisfying Lipschitz condition.Secondlyin consideration of the disturbance term in the systemthe sufficient conditions for the existence of the observer-based controller in two cases are given in the form of Linear Matrix Inequalities (LMI) according to Lyapunov theorythe calculation methods of observer gain matrix and controller gain matrix are obtained and the robust control of the nonlinear system is realized.Finallya numerical example is given to verify the effectiveness of the proposed method.

The cognitive ability of equipment embodies the fundamental attributes of cognitive electronic warfareand it is also the key to effective jamming on complex and intelligent electronic equipment.As a hot technique in the field of artificial intelligencereinforcement learning has the ability of self-learning not relying on priori datawhich is an important approach to multifunctional radar jamming.Based on the review on traditional algorithms of radar jamming decision-makingas for reinforcement learning based radar jamming decision-makingthe principles and status quo of the technology are analyzedand its performance is verified through simulations.Finallya summary is givenand the outlook for the technology is predicted.

Aiming at the visual target detecting and tracking performed by a mobile robotthe background and significance of the research are expoundedand the difficulties in the research are analyzed.Thenaccording to the types of feature representation and the detecting stepsa comparative analysis is made on the advantages and disadvantages of the following four types of target detection algorithmsi.e.traditional target detection algorithmscandidate region based target detection algorithmsregression-based target detection algorithms and reinforcement learning based target detection algorithms.Furthera comparative analysis is conducted on the performance of the following visual tracking algorithms in four stagesi.e.traditional tracking algorithmssparse representation based tracking algorithmscorrelation filter based tracking algorithms and deep learning based tracking algorithms.Finallythe limitations of current methods are summarizedand the directions for future improvement are pointed out.

As the rate of data transmission continues to increasethe data loss caused by the channel becomes more and more serious.Using the traditional Continuous-Time Linear Equalizer (CTLE) to equalize and compensate for the signal can no longer offset the serious signal attenuation caused by the channel.In order to better compensate for the attenuationthe traditional CTLE equalizer is further improvedand a new CTLE based on negative capacitance is proposed.On the basis of the traditional CTLEtwo cross-connected MOS transistors are used to form a negative capacitorwhich is superimposed on the output of the traditional first-level CTLE to form a second-level structure,which can increase high-frequency gain and achieve greater bandwidthso as to provide better compensation for channel attenuation.The simulation results show that at a data rate of 25 Gibit/sthe equalizer with a negative capacitance has good compensation capability.After equalizationthe horizontal opening of the eye diagram reaches 0.9 Unit Interval (UI).The new CTLE based on negative capacitance is helpful for improving the overall data transmission rate.

In this paperthe repetition rate selection of high-repetition-rate laser decoy is studied.Based on the jamming principle of high-repetition-rate laser decoythrough the theoretical analysis and numerical calculation of its jamming pulse energy and repetition ratethe repetition rate application scheme of high-repetition-rate decoy is obtained.The scheme is verified by jamming effect experiment.The research results show that:1) When the high-repetition-rate laser decoy implements decoy jammingthe repetition rate of the high-repetition-rate jamming pulse must be an integral multiple of that of the indicating laser pulse; and 2) At the stage of laser seeker trackingthe high-repetition-rate jamming pulse needs to have higher repetition rate to ensure that there are at least 3~4 jamming pulses entering the time gate; while at the stage of laser seeker searchingthe high-repetition-rate jamming pulse only needs a lower repetition rate to achieve the ideal decoy jamming effect.

A reduced-order observer is designed for a class of linear discrete-time Markovian Jump Systems (MJSs) with unknown input and measurement noise.It can decouple the unknown input and measurement noise simultaneously.The sufficient conditions for the existence of the observer are given in the form of Linear Matrix Inequality (LMI) to ensure the finite-time stochastic stability of the error system.Thenthe estimation of unknown input is realized by using the idea of algebraic reconstruction.Finallya numerical simulation verifies the effectiveness of the proposed method.

For the optical system using refrigeration mid-wave infrared detectora multi-spectral mid-wave infrared convergence system is designed.To solve the problem of external stray radiation introduced by color separation filtera thermal radiation suppression stop is designedand its shape and position are optimized through iterationso as to suppress the external stray radiation of the system.The TracePro simulation shows that this thermal radiation suppression stop can reduce the total stray radiation to 8.8%.It has obvious effects of improving the imaging quality.

ITAE optimal control enables the integral of time multiplied by absolute error to be the minimum in a system.ITAE has the characteristics of third-order no static errorthat isno error for uniform acceleration input.No-static-error ITAE optimal control method is simple and easy to implement in engineering.Howeveras for shipborne rocket launcher systems with time-varying parametersthe effect of directly using ITAE control is not idealbecause the parameters of the controller no longer meet the ITAE optimal transfer function due to the change of system parameters.Thereforeit is necessary to identify the system so that the parameters of the controller can change with the systems parameters.There are many methods for system identificationbut they do not meet the real-time requirements of shipborne rocket launcher.Thereforean easy-to-implement identification method is proposed in consideration of the performance requirements of weapon systems.The proposed algorithm is applied to ITAE parameter tuningand the results show that the proposed algorithm can effectively tune the parameters of ITAE controller of the servo system.

A reduced-order observer is designed for a class of T-S Semi-Markovian Jump Systems (S-MJSs) with general uncertain transition rates.It can simultaneously estimate the system stateactuator fault and sensor faultand completely decouple external disturbances.In the form of Linear Matrix Inequalities (LMI)the sufficient conditions for the existence of the observer are givenand it is proved that the error system is stable in finite time.Finallyanumerical simulation verifies the effectiveness of the proposed method.

ADS-B is an important part of next-generation air traffic control systems.Since ADS-B adopts one-way broadcastthe credibility and reliability of ADS-B data lack effective verification means.Statistical analysis of ADS-B data of different TYPE values is conducted.By calculating the curvature of air tracks formed by air position data and building a confidence data set under the constraints of time windowthe anomaly detection and classification of ADS-B air position data are performedand the relationship between abnormal data and TYPE value is analyzed.The experimental results show that the proposed method can rapidly and accurately identify abnormal air position data.The statistical results show that there is about 1.59 abnormal data on average in every 100 ADS-B air position data.

Current PCB defect detection algorithms suffer from low detection accuracy and slow detection speed.To solve the probleman improved PCB defect detection algorithm based on YOLO v3 network is proposed.Firstlybased on DBSCAN+k-means clustering algorithmre-clustering is performed by using Avg IOU criteria to select the Anchor Boxes that are more suitable for the data set.Secondlytwo residual units are added to the second residual module to improve the networks ability to extract shallow features.At the same timeSE Block module is added to the network to highlight useful feature channels and improve the structure of feature fusion.Finallythe detection module is modified to improve the detection ability on the data set.Experimental results show that the improved algorithm significantly enhances detection accuracy and detection speed on PCB defect data set.

In UAV Ad Hoc networksmulti-priority services are transmitted in parallel,and the existing mechanism of channel busy/idle state division does not consider the fuzzy characteristics of channel states.To solve the problema probabilistic media access control protocol based on fuzzy division of channel busy/idle state is proposed.The protocol mainly adopts two core mechanismsthat isfuzzy division of channel busy/idle state and media access based on probability.The former mechanism can more reasonably divide the channel busy/idle degree by analyzing the fuzzy characteristics of channel states.The latter mechanism can improve the successful transmission rate of the highest priority services and channel utilization rate while effectively reducing data packet collisions under heavy network load.The simulation results show that the protocol can realize the differentiated transmission of multi-priority servicesprovide strict timeliness and reliability for the transmission of the highest priority serviceand improve channel utilization rates under heavy network load.

Data cleaning is an important content in data preprocessingbut problems such as outlier missing and outlier influence exist in current data cleaning technology.A dynamic and fine identification algorithm for outliers based on regression model is proposedin which the regressive values of two data segments ahead of and after the current position are set as referenced values after the elimination of potential outlierswhich is used together with the limits of parameters change rate to give the judgement of outliers.Data cleaning procedure based on regression model is also givenin which steps of coarse identificationfine identification and regressive estimation are adopted to improve the efficiency and effects of data cleaning.A set of real aeronautical data sampled is used to certify the proposed methodand the processing results show that the data cleaning technology based on regression model is able to identify and estimate outliers accurately.