In order to enhance the detection performance of array radar under mainlobe interference,this paper proposes a method of radar anti-mainlobe interference based on fractional domain filtering,which can effectively suppress the mainlobe interferences.The proposed method searches for the mainlobe interference in the fractional domain by using fractional Fourier transform,determines the position of interference based on its peak values.The interference is then filtered in the fractional domain,and the echo signal is further processed by a blocking matrix to better eliminate the mainlobe interference.Simulation experiments validate the effectiveness of the proposed algorithm.

To address the issues of large computation scale and low efficiency of multi-UAV cooperative planning,a Partheno-/Bi-Parent Genetic Algorithm (PBGA) solving model is proposed.This algorithm features an improved encoding method and a single-double parent combined evolutionary strategy.The population evolution is carried out by a partheno-genetic operator,while the bi-parent genetic operator helps to escape from local optima.Simulation results demonstrate the convergence of PBGA.In small-scale and large-scale optimization scenarios,PBGA reduces the convergence iterations by 70% and 64% respectively compared with the traditional genetic algorithm.This approach holds significant reference value for addressing multi-UAV cooperative problems.

Aiming at the problems of low search efficiency,long path and too many turning points in the three-dimensional path planning process of bidirectional RRT algorithm,an improved bidirectional RRT algorithm is proposed on the basis of bidirectional RRT algorithm.The improved bidirectional RRT algorithm is extended from the starting point and the end point at the same time,and a new target bias sampling strategy and dynamic step size strategy are introduced to make the random tree sampling have target directedness,which solves the problem that the original algorithm is too random.The node pruning strategy is used to optimize the generated path and delete the redundant nodes in the path.The B-spline curve is used to smooth the path after pruning optimization.The simulation results show that the improved algorithm can generate a smooth path that satisfies the UAV performance constraints in a three-dimensional environment,which can effectively shorten the path length and improve the path planning speed.

A super-twisting control strategy based on improved sliding mode observer is proposed aiming at the problems of trajectory tracking control of quadrotor UAVs under uncertain disturbances.Firstly,a high-speed third-order sliding mode observer is proposed by improving the traditional sliding mode observer,which can estimate the system state and unknown disturbance more accurately and more quickly.Then,based on the proposed observer,and also on the basis of super-twisting theory and non-singular terminal sliding mode theory,a non-singular,super-twisting terminal sliding mode controller based on the high-speed third-order sliding mode observer is designed to realize tracking and controlling of the trajectory.The stability of the controller is proved by using the Lyapunov theory.Finally,the simulation results show that the proposed strategy has faster response speed and higher precision.

With the development of information technology in aviation industry,air traffic control system is facing the threat of various network attacks.ADS-B has the security problem of being attacked by the network because of its data transmission mode of plaintext broadcast.According to the whole flight operation process of Waiting for landing-Landing-Parking-Queuing for takeoff-Departure (WLPQD),a WLPQD dynamic discrete queuing model is established.The model introduces such parameters as system redundancy and processing speed to simulate the attack scenario.The impact of airport flight delay propagation is studied through the indicators such as the number of affected aircrafts and delayed time.The model shows the delay propagation caused by ADS-B deception attack on landing and take-off flights,and two attack mitigation strategies are proposed.The results show that:1) The stronger the attacking intensity,the severer the delayed flight;2) Both the processing speed improvements on the “ghost aircrafts” and the legal aircrafts are helpful for enhancing the invulnerability of airport system,and the former is more efficient.The research provides a new method for airport system to resist network attacks and enhance invulnerability.

In view of the problems of UAV formations when performing tasks，such as the collisions between UAVs and with obstacles,and formation communication,the UAV dynamics equation is regarded as a second-order integral model,and a collaborative obstacle avoidance control strategy combining artificial potential field method with consistency theory is proposed.First,a variable-gain function is established to reduce the overshoot and steady-state errors,and a consistent control method of UAV formation under a virtual leader structure is designed.Then the idea of segmentation is applied to increase the loop force and the anti-collision force between UAVs to improve the traditional artificial potential field method to solve the issues of local optimization and formation safety,so as to achieve formation collision avoidance and obstacle avoidance.Finally,the consistency control method and the improved artificial potential field method are combined to propose a collaborative formation obstacle avoidance control law,and the small gain theorem is used to analyze its convergence stability.The simulation results show that the UAV formation can achieve accurate obstacle avoidance and restore the expected formation to reach the target position.

Target location distribution model is established to describe two common situations of the submarine in on-call search,including random motion and known approximate course.Aiming at the problem that it is difficult for the Unmanned Underwater Vehicles (UUVs) to describe accurately the target motion status in on-call submarine searching,a target motion model is established based on hidden Markov Model (HMM).It can then update the probability distribution of the target in real time when the target initial probability distribution,transition probability and the detection result are known.The other problem in submarine searching is that the traditional submarine searching methods may not get the largest detection probability in limited time.A submarine searching path planning method based on improved Genetic Algorithm (GA) is designed for the UUV,which adds delete and insert operations on the basis of the traditional GA operations to guarantee connectivity of the search path.Whats more,the elite reservation operation is designed to ensure the fast convergence of the algorithm.The effectiveness of the proposed method is verified through the comparison with such common searching methods as extended position searching,patrol line searching and random searching in simulation experiments.

Aiming at removal of Gaussian noise from color images,a Multi-Channel (MC) optimization model for color image denoising is proposed in the framework of Weighted Nuclear Norm Minimization (WNNM).First,multiple types of noise computation are selected,RGB patches are connected by using the redundancy property of the channels.Then,a weight matrix is introduced to reconcile the image fidelity of the three channels.The given MC-WNNM model is converted into a linear equation constrained phenomenon and is solved by using the Alternating Direction Method of Multipliers (ADMM).Each variable update step has its own closed solution and convergence is guaranteed.Simulation experiments based on real color images for UAV target identification with added noise show that the method has significant advantages over the existing BM3D and WNNM methods.

Considering the low small-target detection accuracy, dense targets and various scale patterns in remote sensing image target detection, small-scale and the higher-scale intermediate information is added to the feature fusion of the weighted Bi-directional Feature Pyramid Network (BiFPN) based on the lightweight network EfficientDet-D0 target detection algorithm, which reconstructs the BiFPN network, makes full use of different scale information, and improves the multi-scale target detection accuracy.At the same time, a feature enhancement module fusing dilated convolution with fast normalized fusion is added in the BiFPN to further improve the detection accuracy.In addition, the Dynamic ReLU activation function with dynamic parameters is used to improve the static Swish activation function in the original network.Without affecting the lightweight feature, the improved EfficientDet algorithm improves the mAP of target detection for public dataset Pascal VOC by 11.90 percentage points in comparison with the original algorithm, which is also better than that of other target detection algorithms.For the remote sensing image dataset RSOD, the existing 936 remote sensing image datasets are augmented by Imgaug data augmentation library, and the migration learning is performed by using the improved model.The target detection result is respectively 88.38% and 96.78% before and after data augmentation, which proves that the proposed method can meet the target detection requirement of remote sensing image in practical applications.

Aiming at the sensor fault problem of Unmanned Aerial Vehicle (UAV) during multi-sensor fusion navigation,a method for diagnosing GPS sensor faults using inertial navigation sensors is proposed to achieve mutual fault diagnosis between sensors.When performing pose calculation on sensor data during UAV navigation,considering the status residual information generated by Extended Kalman Filter (EKF) in the prediction and update process of sensor data in navigation,namely the position information calculated by both inertial navigation and GPS,a sensor fault diagnosis method based on improved Sequential Probability Ratio Test (SPRT) is designed.It improves the sensitivity of the SPRT algorithm to abrupt changes in residual information and the diagnostic sustainability in multiple fault situations.Data simulation experiments show that,compared with traditional methods and other improved algorithms,this method can detect the time when faults occur and disappear quickly and accurately,and continuously diagnose faults,which improves the flight safety of UAVs greatly.

MAGIC CARPET is a very successful carrier landing control technology for carrier-based aircrafts in the United States in recent years,and it will be the main landing mode in the future.MAGIC CARPET has excellent landing performance,but it is greatly affected by deck motion.Therefore,it is necessary to study the strategy for addressing deck motion.Firstly,carrier deck motion and optical glide slope stabilization modes are analyzed.Then,MAGIC CARPET carrier landing is studied respectively for the inertial stabilization and the line stabilization of glide slope,and relevant conclusions are obtained.Results of theoretical analysis and real-time simulation of this article have good consistency with the results of the US military flight test.Finally,the selection method for the stabilization mode of the glide slope is analyzed.The research results provide reference for aircraft landing and direct force control.

Radio Frequency (RF) stealth radar can improve the electronic defense capability of radars fundamentally,which is a research hotspot of current aviation equipment,and the key of it is the design of RF stealth signal.Anti-sorting signal is an important RF stealth signal,and the design method of anti-sorting signal is one of the difficult points in RF stealth signal design.By analyzing Sequential Difference Histogram (SDIF) sorting algorithm,the main sorting algorithm of Radar Warning Receiver (RWR) and other real-time electronic reconnaissance equipment,the failure principle of Pulse Repetition Interval (PRI) estimation error is pointed out when PRI signal changes rapidly in a large range.Then,an anti-sorting signal design method for RF stealth radar based on pulse group agility is proposed.In other words,through PRI stride agility of pulse group signal,the main sorting algorithm output of RWR and other real-time broadband electronic reconnaissance equipment may produce large error or even complete error.Simulation and experiment prove that the signal designed based on the above method can effectively counter RWR main sorting algorithm,and the objective of radar anti-sorting is achieved.

A remote sensing image change detection method based on multi-scale feature cross-fusion is proposed to address the issues of missed detections and false alarms for small targets,as well as insufficient segmentation of fine details in traditional change detection methods.The method adopts an encoder-decoder structure with the introduction of Multi-Scale Feature Fusion (MSFF) module and Multi-Scale Attention (MSA) mechanism between the encoder and decoder.The former is used to aggregate information from different scales,while the latter captures differences and correlations between different scales.A Refinement Output (RO) module is introduced at the end of the encoder,using parallel convolutions with dilation and atrous convolutions to further refine the fine details and reduce edge information loss.Experimental results on the LEVIR-CD dataset demonstrate that the proposed method can effectively identify small targets while preserving fine details,leading to significant improvements in accuracy,F1 score,intersection-over-union,and overall accuracy.

In order to evaluate the human-computer interaction efficiency of the command and control system more accurately,this article establishes an evaluation index system based respectively on the three elements of human-computer interaction,i.e.,command personnel,command and control system,and interaction equipment.The entropy weight method is combined with CRITIC to assign weights to each index,and the Fuzzy Comprehensive Evaluation (FCE) method is used to comprehensively evaluate two types of command and control systems.Finally,GOMS model and eye tracking technology are used to verify the evaluation method from the aspects of task completion efficiency and cognitive load of the commander,etc.The results indicate that this method has certain reference value and significance,which provides a scientific and convenient evaluation method for human-computer interaction effectiveness of the command and control system.

With a focus on meeting the quantitative analysis requirements for situational awareness and operational decision-making in maritime information warfare,a method utilizing improved fuzzy-AHP approach is proposed for assessing the impact of complex electromagnetic environments on naval equipment.First,the paper analyzes the “four domains” characteristics of the electromagnetic environment in naval battlefields.Based on the conclusions drawn from this analysis,two types of evaluation indicator systems are established:a general and a specific evaluation indicator system for evaluating the impact of electromagnetic spectra on naval equipment.To enhance the traditional fuzzy-AHP method,the bat algorithm is employed to effectively mitigate errors and subjectivity in the process of determining indicator weights.The applicability of the proposed method is demonstrated through its application to the assessment problem of equipments response to complex electromagnetic environments.Additionally,a naval exercise operation is designed,evaluated,and analyzed to validate the effectiveness of the model.The findings of this study provide valuable recommendations and requirements for command decision-making in maritime operations.

Remote sensing images have the characteristics asblurred terrain capture and complex background environment,which leads to a problem of low accuracy in identifying large ground-level objects.To solve this problem,an improved network model based on YOLOv5s is proposed.The proposed model adjusts YOLOv5s network models backbone extraction network and neck multi-scale feature fusion network,and introduces Swin Transformer for obtaining more feature information about the target objects.Additionally,the model prunes the modules in the main network and adds coordinate attention mechanism to enhance feature extraction and fusion effects.The proposed model is tested on small target recognition by using remote sensing dataset,and mAP value of the improved YOLOv5s network is 0.8375,which is 0.0225 higher than that of official YOLOv5s network model.Experimental results show that the proposed model effectively improves the recognition accuracy,recall rate,and mAP value in comparison with YOLO series network and EfficientDet model,and it reduces the training time by 1/12 in comparison with the YOLOv5s model.