Attitude measurement plays a key role in both civil and military fields.It is an important part of realizing and developing the automation and intelligence of instruments and equipment onboard a vehicle, a ship or an airplane.Therefore, it is of practical value and wide application prospect to design an attitude measurement system that can meet the corresponding requirements for specific application scenarios, study the data fusion mechanism of different types of measurement sensors, explore and optimize the sensor layout and system structure, and realize accurate, stable, robust and adaptable attitude measurement.The research progress of the measurement system based on vision and dual inertial sensors integration is introduced.The development status of key technologies in the integrated measurement system is summarized, such as system structure design, external parameter calibration of visual and inertial sensors, differential research on dual inertial sensors and data fusion algorithms.According to the existing research, the prospect of vision and dual inertial sensors integration attitude measurement is given.

A robust affine formation control algorithm with prescribed performance is proposed for multi-agent systems with unknown external disturbances.In the directional graph, the follower distance error dynamics model is given by using the Laplace matrix of symbols according to the leaders state.Then, by using the input to state stability and Lyapunov stability theorem, the convergence and stability of the proposed control algorithm are proved, the external disturbances are restrained, and it is guaranteed that the error state of the system converges to the prescribed performance function, which ensures fine transient behavior of the system.Finally, the simulation results show that the follower can follow the leader under external interferences, and the validity of the proposed algorithm is verified.

In the visual servo process, the quadrotor UAV is prone to be interfered.To solve the problem, a nonlinear anti-disturbance controller for image-based visual servo control of the quadrotor UAV is designed by using an integral backstepping sliding mode control method.Image moments are chosen as features.The virtual camera plane is used to improve the dynamic model in the presence of model uncertainties and external disturbances.As for the linear velocity of virtual plane that is hard to measure directly, a linear velocity estimator is designed by using the backstepping method to improve the accuracy of control.The stability of the proposed global integral backstepping sliding mode controller is proved by using Lyapunov theory.The simulation experiment results demonstrate the effectiveness and robustness of the designed controller.

The traditional leader-based formation control method of multi-AUV system is susceptible to environmental disturbances and easy to collapse.To solve the problems,an improved virtual leader method based on artificial potential field is proposed.A four-layer framework of the formation system is designed, and the layers of formation design and behavior control are described.Appropriate artificial potential field functions are designed for the following three scenarios, that is, the repulsion and attraction exerted by the virtual leader on AUV members, the repulsion and attraction between AUV members, and the repulsive force exerted by obstacles on the AUV, and the constraints brought by the maximum communication distance are taken into consideration.Through simulation verification, the formation trajectory and position deviation curves of the traditional leader-follower method and the improved virtual leader method are compared and analyzed.It is proved that the designed algorithm can complete the formation control task, and the formation control effect is better than that of the traditional leader-based method.

Aiming at the insufficient feature extraction of the neck feature extraction network PANET of current YOLOv5s, and the conventional convolution Conv consumes a large amount of parameters and calculations, a lightweight target detection algorithm (RFBG-YOLO) is proposed.Firstly, in order to improve the recognition effect of the detector, a multi-branch atrous convolution structure RFB-Bottleneck is proposed to improve the feature extraction ability of PANET, thereby improving the detection accuracy of the model.Then, in order to make the model more lightweight, GhostConv convolution is introduced to reduce the amount of model parameters and improve the detection speed.The results on the PASCAL VOC data set show that in the case of small impact on the detection speed, the mAP@0.5 of the RFBG-YOLO algorithm is 80.3%, an increase of 2.2 percentage points compared with that of YOLOv5s algorithm; mAP@0.5∶0.95 is 55.1%, an increase of 4.2 percentage points compared with that of YOLOv5s algorithm; the amount of model parameters is 5.2 MiB, a reduction of 2.0 MiB compared with that of YOLOv5s algorithm.Therefore, the proposed RFBG-YOLO algorithm has a high enough detection accuracy while ensuring the light weight of the model, which can meet the requirements of detection accuracy in the scenario of lightweight target detection.

Aiming at the complex environment of indoor environment where a lot of sundries are stored or outdoor urban scene with many high-rise buildings, this paper proposes a method, which uses Theta* algorithm for global path planning and improved Dynamic Window Approach (DWA) for tracking path nodes, to realize the path planning of the quad-rotor UAVs in three-dimensional environment.Compared with the method of making global static path planning first and then making path C2 continuity optimization, this method can realize the avoidance of unknown obstacle information in the map, and the generated curve conforms to the dynamic constraints of UAV, which ensures that the UAV can follow the path without attitude disorder caused by the drastic change of speed.Finally, the algorithm not only generates the planned obstacle-avoidance path, but also generates the speed change of the motor.The proposed method can realize trajectory tracking only by tracking the change of motor speed without interference, which simplifies the control of UAV.

The interference of specular reflection light such as large-area flare and light spot on target surface during imaging process directly hinders the vision performance like detection and matching.A specular reflection separation algorithm based on polarization imaging is proposed, which suppresses the impact of specular reflection on target surface.By analyzing the polarization imaging mechanism of dichromatic reflection model, and using the polarization characteristics difference between diffuse reflection and specular reflection, the inversion mechanism of the intensity of diffuse reflection based on Stokes parameters is proposed.Then, the constraint of spatial CbCr-chromaticity vector is constructed, and the physical isolation of specular light in CbCr space is realized.The traversal least squares method is applied to obtain the reflection parameters.Thus the specular component is effectively removed, and the inherent local details on target surface are restored.Experimental results show that the proposed algorithm can achieve accurate specular reflection removal.Meanwhile, the original color, structure and texture features are highly preserved.The algorithm is scene-adaptively robust with high recovery capability.

To countermeasure semi-active laser guided missiles, high-repetition-rate laser interference is an effective means.In order to analyze the interference efficiency of high-repetition-rate laser to missiles adopting different pulse admission techniques, a model describing high-repetition-rate laser interference efficiency is established.The success probability of the first interference of the seekers wave gates, the success probability of the second interference, and the interference times required to deflect the target indication signals out of the wave gates are simulated, and the effects of high-repetition-rate laser frequency, wave gates width and other factors on the interference efficiency are studied.The simulation results show that as for missiles that adopt the first (last) pulse admission technique, the higher the interference frequency and the wider the wave gates width, the higher the success probability of interference, the fewer times required to deflect the target indication signals out of the wave gates.When the missile adopts the optimal timing pulse admission technique, in the first interference, the higher the interference frequency and the wider the wave gates width, the higher the success probability of interference.Under the condition of successful first interference, the success probability of the second interference tends to level off with the increase of the frequency of interference laser.The optimal jamming frequency required to deflect the target indication signals out of the wave gates is located near the reciprocal of the wave gates width.The research results of this paper can provide reference for equipment development and operational application.

To solve the problem that the traditional vision SLAM is not fully capable of understanding the semantic information, semantic vision SLAM uses semantic landmarks to improve the localization accuracy of the robot.Accurate association of semantic landmarks is the key to deep localization and navigation of the robot, and incorrect association will lead to loss of localization.To address the problem of high association ambiguity caused by dynamic perturbation and observation noise perturbation, a method combining nonparametric clustering with stochastic approximate inference is proposed to improve the accuracy of semantic landmark association, and accurate localization is realized by correct data association.The results of simulations and experiments on KITTI data set show that, the proposed algorithm can improve the accuracy and robustness of data association of semantic landmarks under noise perturbation, fuse the semantic and geometric information to optimize the poses of the robot and semantic landmarks, and improve the localization accuracy of the robot.

In the context of electronic reconnaissance satellite supporting anti-sea operation, in order to realize the full potential of satellite, a model for satellite operation effectiveness evaluation is proposed based on Support Vector Regression (SVR) optimized by Firefly and Sparrow Search Algorithm (FASSA).Firstly, Firefly Algorithm (FA) is used to improve Sparrow Search Algorithm (SSA).Then, the evaluation index system for operation effectiveness of electronic reconnaissance satellite is established.Finally, FASSA is used to optimize relevant parameters of SVR, and the operation effectiveness evaluation model of electronic reconnaissance satellite is established according to the effectiveness evaluation index system.The simulation results show that the proposed model has high evaluation accuracy, which is better than other models on various indices.

This paper introduces the technical characteristics of blockchain and its technical advantages in the military application of UAV swarm, and proposes a blockchain architecture for the military application of UAV swarm. Then, it comprehensively sorts out the current research status of the military application of UAV swarm based on blockchain, and analyzes the challenges and solutions of the current blockchain technology in the military application of UAV swarm, which provides research ideas for the future military application of UAV swarm based on blockchain technology.

Compared with that of the medium or large targets, the detection accuracy of small targets with pixels less than 32×32 is poor due to low pixels and lack of feature information, which results in low accuracy of underwater robots, UAVs and other facilities in classification and positioning of small objects.In order to improve the accuracy of the relative facilities in small target detection, analysis is made on the current situation of general deep-learning target detection algorithms at first.Then, the problems hindering the development of the small target detection technology are expounded, and the algorithms of data enhancement, feature fusion, resolution enhancement and context information that can improve the accuracy of small target detection are analyzed.Finally, the performances of AP and APs of all the algorithms in MS COCO data set are summarized, and the future development direction of small target detection is prospected.

Efficient development of complex civil avionics system needs support of several kinds of integrated verification platforms.Firstly, the category of integrated verification platform is confirmed through analyzing the engineering activities of complex avionics system.Secondly, functions of integrated verification platform are analyzed, and the definition of universal functions is given.Thirdly, current status of integrated verification platform is analyzed by using the method of dimensional analysis, and optimization ideas are provided.Finally, top-level planning and unified design are performed for versatile device-level integrated verification platform, a universal design model is developed, and implementation specifications of the model are provided.

In the case of poor Global Positioning System (GPS) signals in recent years, optical flow is used for UAV positioning and navigation, whose accuracy is not sufficient.To solve the problem, an improved optical flow tracking algorithm is proposed.Oriented FAST and Rotated BRIEF (ORB) algorithm is used to extract feature points and send them into Lucas-Kanade (LK) pyramid.The feature points of the next frame are estimated by using bidirectional tracking, and the feature points of the current frame are verified and supplemented at the same time.After the elimination of error points, the coordinates of the point set composed of the preliminary credible points and the supplementary credible points are obtained to estimate the UAV pose.After extracting feature points from a variety of environments, real machine experiments are conducted.The experimental results show that the improved optical flow tracking and predictive control method has better positioning and tracking effects and better accuracy in comprehensive environments, which can be better applied in more complicated environments such as military use.

Airborne infrared countermeasures system is developing towards systematization and integration.As an important jamming means in infrared countermeasures, the cooperative usage of infrared jamming projectile and directional infrared countermeasure system is a key.By analyzing the interference mechanism of infrared jamming projectile and directional infrared countermeasure system, an integrated infrared countermeasure system model is established, and the procedure and rules of cooperative usage are studied.The effectiveness of the cooperative usage strategy for orthogonal quad-element infrared seeker is verified by simulation.

Time-Triggered Ethernet (TTE) for aeronautical and astronautical applications places stringent requirements on real-time communication and message security.Efficient global static scheduling technology is conducive to fully guaranteeing the service quality of different messages.Considering that the Time-Triggered (TT) messages with the highest real-time performance determine the transmission time of low-priority Rate-Constrained (RC) messages, a TTE optimal scheduling technology based on rapid increment is proposed.By rapidly incrementing the basic scheduling unit, backtracking to correct the multi-hop delay, and optimizing the static schedule of TT messages, the transmission performance of RC messages is improved.Finally, the network scheduling algorithm is applied to the network topology model of the time-triggered avionics system for verification.The simulation experiment shows that the TTE optimal scheduling technology effectively improves the real-time performance of the network under the condition that the TT messages satisfy the transmission constraints.The average end-to-end latency of RC messages is optimized by 14.08%, and the worst end-to-end latency is optimized by 22.86%.

Image segmentation plays an important role in daily life.Traditional K-means image segmentation is random and easy to fall into local optimization, which greatly reduces the quality of segmentation.In order to overcome these shortcomings, a K-means image segmentation based on Halton sequence improved Manta Ray Foraging Optimization (HMRFO) is proposed.HMRFO uses the Halton sequence to initialize the population to make the individual positions fully uniform, and then introduces Refracted Opposite-Based Learning (ROBL) to improve the global search ability of the algorithm, and finally introduces a new Gaussian mutation strategy to reduce the probability of the algorithm falling into local optimum.Five algorithms are compared in six benchmark test functions, which verifies the effectiveness and feasibility of HMRFO.It is applied to K-means image segmentation and compared with other four algorithms.The results show that HMRFO optimized K-means has better segmentation quality and generalization ability.

Considering that in Inverse Synthetic Aperture Radar (ISAR) imaging of high-speed moving targets, the phase compensation method based on traditional Linear Frequency Modulated (LFM) parameter estimation has such problems as complex calculation and large errors, a new approach based on Convolutional Neural Network (CNN) is proposed to estimate the frequency modulation slope of chirp signals.First, Wigner-Ville distribution time-frequency analysis method is used to generate time-frequency image training set for LFM signal with a certain range of frequency modulation slope.Secondly, the time-frequency image of the high-speed moving target echo signal processed by Wigner-Ville distribution is input into the CNN to identify the frequency modulation slope of the echo signal.Then, the target velocity is inversed by the identified FM slope, the compensation signal is constructed and phase compensation is made to the echo signal.Finally, clear ISAR image is obtained through the processing of Range-Doppler imaging algorithm.Simulation results show the effectiveness of the proposed method.

The parameter changes of optical lens caused by temperature changes may result in defocusing of the optical system, and the purpose of athermalization design is to compensate for the influence of temperature changes, and match the detector with the image plane.Normal athermalization design methods mainly study the influence of temperature on refractive index, and the change of position, angle and surface of the optical elements due to structural deformation is not taken into full consideration.A detailed analysis is made to the influence of temperature on optical system in consideration of structural-thermal-optical effects.Then, an optical athermalization design method based on structural-thermal-optical integration simulation is studied, and the effectiveness of this method is verified by a passive athermalization design of Cassegrain optical system.

Aiming at the problem that it is difficult to calibrate the magnetic field center of magnetic beacon in the location of ultra-low frequency magnetic field generated by magnetic beacon, a high-precision magnetic beacon calibration method based on the distribution characteristics of magnetic induction intensity of magnetic dipole is proposed.In order to describe the magnetic field distribution of the magnetic beacon at any position in space, the equivalent magnetic dipole model of the magnetic beacon is established.Through the relationship between the components and positions in different directions of the magnetic induction intensity, the central position of the magnetic field of the magnetic beacon coil is solved, and the adaptive exponential smoothing algorithm is used to reduce the influence of its own fluctuation on the magnetic induction signal, thus to realize high-precision calibration of the magnetic beacon magnetic field center.The finite element simulation of the calibration method shows that the calibration accuracy of the magnetic field center using the distribution characteristics of magnetic induction intensity is at the millimeter level.Through the comparison of the positioning accuracy before and after calibration, it is found that the positioning accuracy of the calibrated magnetic beacon is effectively improved.