In recent decades,the asymptotically optimal Rapidly-exploring Random Tree* (RRT*) algorithm has attracted extensive attention.In order to solve the problems of slow convergence and high costs of generating path,an improved APF-Informed-RRT* fusion algorithm for UAV trajectory planning is proposed.In combination with the Informed sampling strategy,the algorithm constrains the random points in the elliptic space,and improves the search efficiency.After the new algorithm finds the nearest node,the improved APF is introduced to generate high-quality new nodes.The target point and random sampling point are attractive to the nearest node of the growing tree,and the obstacle is repulsive to it.Then,the direction of resultant force is taken as the growth direction of the random tree to solve the problem of local minimum value and greatly decrease the convergence time.Compared with RRT* and Informed-RRT*,the new algorithm is superior and effective.

To solve the problem of two-dimensional trajectory tracking of fixed-wing UAV under crosswind conditions,a trajectory tracking algorithm with adaptive guidance length is proposed based on nonlinear guidance law.Firstly,a kinematic model of the UAV after introducing wind disturbance is established,the nonlinear guidance law with a fixed guidance length is theoretically analyzed,and its wind resistance performance is discussed.Then,based on the performance and flight velocity of the UAV itself,a method of adaptive guidance length is proposed,and the side offset distance integral is introduced into the lateral acceleration command to offset the crosswind interference.To solve the problem that the trajectory error of the UAV is too large when the waypoint is switched,an arc waypoint switching strategy is designed.Finally,the numerical simulation is carried out in the case of crosswind interference.The results show that the method can effectively suppress crosswind interference,and has high tracking accuracy at waypoint switching.The precise tracking of the desired trajectory of the fixed-wing UAV under the condition of crosswind interference is realized.

For the problem of UAV path planning in complex multi-obstacle environment,an algorithm based on Improved Particle Swarm Optimization (IPSO) is proposed.Firstly,the stability of the algorithm is enhanced by unifying the obstacle environment modeling,optimizing the fitness function,and using chaotic particle initialization to diversify the particle swarm.Then,the acceleration constant of the traditional Particle Swarm Optimization (PSO) is replaced by the adaptive acceleration coefficient to avoid falling into local minimum,while improving the efficiency of the algorithm converging to the global optimal solution.Finally,the encoding method of the particle search trajectory in the traditional PSO is replaced by the encoding of UAV motion,which is used to improve the optimality of the solution and search for the optimal path solution.The simulation results show that the IPSO can effectively solve the problems of the traditional PSO in UAV path planning in the complex multi-obstacle environment.In comparison with Gray Wolf Optimization (GWO),Differential Evolution (DE),Quantum Particle Swarm Optimization (QPSO) and traditional PSO,the improved algorithm has significantly improved the path optimization accuracy and stability in different scenarios of static environments.In comparison with Dynamic Particle Swarm Optimization (DPSO), the new algorithm can also be better adapted to the dynamic environment.

To solve the current problem of dynamic threat assessment of radiation sources,a threat assessment algorithm based on attribute reduction-improved TOPSIS is proposed,and a dynamic threat assessment model is constructed by introducing the combination weighting method and the time weight calculation method based on time degree.Attribute reduction is carried out according to the dominant relationship of the information system,and then the combination weighting method is used to calculate the attribute weight.The TOPSIS method is improved based on the measurements of the Euclidean distance,gray correlation degree and generalized Mahalanobis distance,and the weighted comprehensive proximity degree is calculated.In combination with the time weight,the final threat ranking is realized.The simulation results have verified the rationality of the proposed algorithm.Compared with the traditional evaluation method,the proposed algorithm has higher reliability and the parameters can be adjusted according to the preferences of decision makers with strong flexibility.

To solve the problem of multiple missiles attacking a maneuvering target,a fixed-time cooperative guidance law with attack time constraints is proposed.Firstly,the geometric model describing the relative motion between the missiles and the target in the longitudinal plane is established.The relationship between the missiles radial acceleration in line-of-sight direction and time-to-go is analyzed.The fixed-time cooperative guidance law is designed based on first-order system consistency.Secondly,the targets maneuver is seen as the bounded external disturbance,and a fixed-time convergence observer is used to estimate it.The estimated value is applied to the design of the cooperative guidance law to reduce the chattering phenomenon caused by the upper bound of the disturbance.Finally,the effectiveness of the fixed-time cooperative guidance law is verified by numerical simulation.

Strong sea clutter is the interference term that has the greatest impact on the target detection performance of sea radar,and the performance of the existing single detection quantity and multi-feature joint detection algorithms is extremely unstable.To solve the above problems,a sea radar target detection algorithm based on clustering is proposed.The algorithm extracts three feature quantities,that is,Relative Amplitude Variance (RAV),Relative Average Power (RAP) and Relative Vector Entropy (RVE).The k-Nearest Neighbor (kNN) algorithm in the clustering algorithm is modified to complete a kNN detector with controllable false alarm.In this feature space,the kNN detector is used to separate the target from clutters.The experimental results of measured radar data show that when the observation time is 0.512 s and 1.024 s, the average detection probability of this algorithm is 56.2% and 58.3% higher than that of the fractal based detector respectively (in HH polarization mode),and 29.2% and 31.3% higher than that of the three-feature-based detector respectively.It can be concluded that this algorithm can realize the sea radar target detection under complex sea conditions,and the detection effect is obviously better than that of the detector algorithm based on three features.

A Synthetic Aperture Radar (SAR) image target classification method is proposed by combining Bi-dimensional Variational Mode Decomposition (BVMD) with Convolutional Neural Networks (CNN).The original SAR images are decomposed by BVMD into a series of modes,which reflects the global and detail information of the targets.Afterwards,a suitable CNN architecture is designed to classify different modes and output the posterior probability vectors.Then,those posterior probability vectors are fused by using the Bayesian theory.Finally,the target label is decided according to the fused probabilities.Through the combination of the advantages of BVMD and CNN,the proposed method improves the classification performance comprehensively.In the experiments,the proposed method is tested based on MSTAR dataset under four typical scenarios and compared with the existing methods.The results show the superiority of the proposed method.

For most traditional defogging algorithms,there are problems such as color imbalance and low visibility after processing images.In this paper,a bilateral filtering transmittance optimization algorithm based on fog line priori is proposed.Firstly,the pixels are clustered into fog lines in RGB space,and the adaptive module is introduced to predict the atmospheric light value.Secondly,the transmittance of the image is preliminarily optimized according to the combination of atmospheric scattering model with context regularization.At the same time,the transmittance is corrected based on the minimum channel,which makes the transmittance map smoother and prevents the transmittance difference between adjacent depth-of-field areas from being too large.Then,the transmittance is optimized for the second time through bilateral filtering to make it more accurate.Moreover,the atmospheric light value and transmittance are input into the atmospheric scattering model for defogging to obtain a clear image.Finally,the defogged image is color-enhanced to improve the color authenticity and brightness of the image.The experimental results show that this method improves the subjective visual effect to the human eye.This method has significant advantages in the objective evaluation indicators such as Structural Similarity (SSIM),Peak Signal-to-Noise Ratio (PSNR),corner detection number,Universal Quality Index (UQI),Natural Image Quality Evaluator (NIQE) and processing time.

Airborne integrated self-defense systems are the “armor” of fighters and an important means to ensure the vitality of fighters and improve the combat effectiveness of fighters.The airborne integrated self-defense system can be divided into three parts:threat detector,countermeasure device and integrated controller.It has the characteristics of multiple functions,real-time performance,strong adaptability and high degree of automation.This paper introduces the composition and characteristics of airborne integrated self-defense systems,analyzes the composition and function of the mainstream integrated self-defense systems abroad,and predicts the development trend of airborne integrated self-defense systems.

A remote sensing small target detection algorithm based on the improved YOLOv4 is proposed to address the problems of low detection accuracy and serious missing detection of small targets in remote sensing images.Firstly, the feature extraction network is improved by removing the deep feature layer to reduce semantic loss.Secondly, the lightweight attention mechanism is fused with the RFB-S structure to expand the perceptual field and enhance attention to important information, thus improving the detection precision.Finally, the Focal Loss is used to avoid the imbalance between positive and negative samples and suppress the background targets to further enhance the detection effect.The experimental results on the RSOD dataset show that the improved algorithm has an average detection precision of 96.5% and a recall rate of 87.2%, which significantly improves the detection effect and effectively avoids the phenomenon of small target miss detection, and is of great significance to small target detection in remote sensing images.

To countermeasure semi-active laser-guided missile,laser angle deception interference is an effective means.In order to improve the combat effectiveness of laser angle deception interference and save interference resources,the probability model of the interference signal admitted by the wavegate of the seeker is established on the basis of theoretical analysis,the optimal lead time for angle deception interference of the semi-active laser-guided missile is studied,and a laser angle deception interference method of progressive interference lead time is proposed.The simulation results show that there is a correspondence between the ratio of the wavegate width to the error of the indicator signal and the probability of successful guidance.When the wavegate width is fixed,there is an optimal interference lead time so that the interference lead probability is the greatest.The interference efficiency of the laser angle deception interference method of progressive interference lead time is high,which has certain practical value.

In order to solve the problem of GNSS signal loss caused by GNSS faults or external environment occlusion in INS/GNSS integrated navigation system,an integrated navigation algorithm based on Random Forest Regression (RFR) factor graph is proposed.Firstly,the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) are modeled by using factor graph method,and the factor graph model of INS/GNSS integrated navigation is built.Secondly,the random forest theory is introduced to build the random forest,and the training is carried out when the GNSS signal is effective,and the GNSS signal output when the satellite navigation fails is simulated.Finally,a simulation experiment is designed,and the results show that the improved factor graph algorithm improves the navigation accuracy by about 10%~15% compared with the federated Kalman filter algorithm.Meanwhile,the proposed RFR factor graph algorithm can still maintain high accuracy in the case of GNSS signal loss.

To solve the problem of constant velocity control of piston engine for the medium-sized unmanned helicopter,a fuzzy Active Disturbance Rejection Control (ADRC) law is designed with feed-forward compensation.The mathematical model of the engine is established by introducing the rotor load characteristics,and the collective pitch control and forward flight velocity are taken as known disturbances for feed-forward compensation.The fuzzy PID controller is designed to adapt to the nonlinear characteristics in the complicated work process of the piston engine.The rotors aerodynamic change and the wind disturbance are regarded as unknown disturbances,and an Extended State Observer (ESO) is designed to perform estimation and feedback compensation of disturbances in real time.It is verified by numerical simulation that the control law designed in this paper can effectively improve the anti-interference ability and control accuracy of the velocity controller.The ground driving and flight test for the engine are conducted.The results show that the control error of the rotor velocity of the unmanned helicopter is less than 5.0 r/min during the flight test,and the dynamic tracking performance is significantly improved.

When the aircraft is on a cruise flight,it is required that the aircraft photoelectric system can automatically scan the ground targets,realizing seamless scanning in the region of flight path.A seamless ground scanning control algorithm for aircraft is studied.According to the conditions of flight velocity,flight height,the detectors field of view and detection distance,a group of optimal control parameters of the aircraft photoelectric system such as azimuth scanning range,angular velocity of azimuth scanning and pitch angle is calculated.Finally,the correctness and validity of the control algorithm are verified through Matlab simulation.

A small number of test points are selected for the traditional HUD display accuracy test.If the test results are not out of tolerance,it is judged to be qualified,and it is impossible to prove whether it meets the overall requirements.Therefore,the scheme needs to be improved.A simple random sampling model is designed by using the inferential statistical theory in mathematical statistics and in combination with the characteristics of HUD field of view.A sampling test is conducted,and the overall requirements are inferred based on the sample test results.Firstly,a random sample model with uniform distribution is designed by using the simple random sampling theory.Then,the hypothesis test theory is adopted to study the decision-making process of display precision,and the relationship among display precision out of tolerance,the minimum sample size and the maximum allowable number out of tolerance is determined.Finally,a verification scheme is designed,and the test results prove the rationality of the improved scheme.The improved scheme can take into account the risks of both the manufacturer and the user.

Flight action recognition is of great significance to evaluate the quality of flight training and improve the pilots driving skills.In the flight action sequence data,the flight state data at a certain time and some flight parameter data corresponding to the state make a very important contribution to flight action recognition,but the traditional attention mechanism only focuses on the contribution value of the upper-layer state features and ignores the influence of the lower-layer features.In order to effectively extract the key feature representation of flight parameter data,the Focus Attention (FA) mechanism is proposed,which extends the traditional attention mechanism,further focuses attention and learns the contribution of its lower-layer features to the upper layer.At the same time,the FA mechanism is extended to the BiLSTM network and the FA-BiLSTM network model is proposed.The model not only focuses on the flight state data at the critical time in the flight action sequence,but also can learn the key flight parameter data in the flight state.Experiments show that this method effectively improves the accuracy of flight action recognition,and the weighted average accuracy reaches 94%.

The heavy equipment airdrop mission is complex,risky and uncertain.The safety problem in the heavy equipment airdrop mission is regarded as a control problem,and it is the first time to conduct safety analysis of the heavy equipment airdrop mission based on STPA.The Unsafe Control Actions (UCA) in the heavy equipment airdrop mission are identified by system analysis,and the control feedback structure of the heavy equipment airdrop mission is established to analyze the cause of UCAs.The safety calculation framework of the heavy equipment airdrop mission is constructed,several typical UCAs are taken as the calculation analysis objects,and the quantitative safety analysis of heavy equipment airdrop is carried out.The results show that STPA can carry out safety analysis of heavy equipment airdrop effectively,and the quantitative calculation results can provide certain guidance for the crew to make scientific decisions.

Nanosecond pulsed lasers pose a serious threat to photoelectric sensors widely used in the battlefield.Optical limiting materials not only prevent the strong laser from passing through,but also do not affect the low-energy signal light,which is a research hot-spot in laser protection field at present.To meet the application requirements of strong laser protection for optoelectronic sensors in visible-near-infrared band,based on metal phthalocyanine compound and the design idea of enhancing the electron cloud density of its conjugated system,a new type of high-performance optical limiting material,tetra(4-(2-phenylpropan-2-yl)phenoxy) indium chlorophthalocyanine,is prepared and it is tested and characterized.Transient absorption spectrum experiments show that tetra(4-(2-phenylpropan-2-yl)phenoxy) indium chlorophthalocyanine has strong and long-life excited state absorption.The optical limit performance test shows that the optical limit start threshold is 8 mJ/cm2,the optical limit threshold is 0.06 J/cm2,and the damage threshold is greater than 4 J/cm2.When the input energy current reaches 3 J/cm2,the transmittance of the optical limiting device decreases to 6%.It can be seen from the test results that the material has low optical limit start threshold and optical limit threshold, high damage threshold and high linear transmittance in visible-near-infrared band,which indicate that it owns excellent optical limit performance,and has a high application prospect in laser protection of photoelectric system.

The number of unmanned equipment is generally large.Remaining Useful Life (RUL) prediction is especially important due to long mission time and harsh environments.The prediction accuracy of comprehensive performance indicators sequence using a single model is low.In order to solve this problem,an RUL prediction method based on Kalman fusion model is proposed.Firstly,the area maximum method is used to extract the degradation phase of the comprehensive performance indicators of key components of unmanned equipment.Secondly,the GM(1,1) model with exponential characteristics,the linear support vector machine SVR model,and the nonlinear Extreme Learning Machine (ELM) model are used to predict the comprehensive performance indicators.Each model can capture different characteristics of the comprehensive performance indicators.Finally,the Kalman framework is used to fuse the prediction results of the three models according to the principle of iterative least squares.The experimental results show that the prediction method of Kalman fusion model can significantly improve the prediction accuracy of comprehensive performance indicators.Compared with that of single models of ELM,SVR and GM(1,1),the fitting accuracy is increased by 16.96%,1.61% and 39.84% respectively,and the prediction accuracy is increased by 45.06%,38.35% and 74.12% respectively.