To realize the tracking of maneuvering targets in the presence of long-range suppression jamming, the influence of long-range, high-power suppression jamming on radar detection and measurement accuracy is analyzed.A new measurement model is introduced to simulate the temporary disappearance of the targets caused by the decrease of detection probability of the radar in the presence of suppression jamming.The measurement in spherical coordinates is converted to measurement in right angle coordinates by using the decorrelated and unbiased measurement conversion method.Then, a maneuvering target tracking algorithm under suppression jamming on the radar is established based on the adaptive federal filter.The simulation results show that, the proposed method can maintain stable and accurate tracking of targets in a variety of maneuvering situations under long-range suppression jamming, which is obviously superior to the existing methods.

The classical DS evidence theory cannot effectively conduct high-conflict evidence fusion.To solve the problem, a high-conflict evidence fusion method based on average evidence and focal element distance is proposed.Firstly, Pearson correlation coefficient is used to calculate the correlation between evidence bodies, and the weight of evidence is defined to calculate the average evidence.Then, the average evidence is used to calculate the focal distance of each evidence element, and the conflict is redistributed to each focal element BPA according to the focal distance to construct a new evidence body.Finally, the traditional Dempster combination of the new evidence body is conducted to obtain the fusion result.Simulation experiments show that the new method has high accuracy and fast convergence rate when dealing with high-conflict evidence, which provides a good solution to the problem of high-conflict evidence fusion.

It is an important research direction to classify military images.The classification accuracy based on traditional visual features is not high due to the high similarity of different military targets in complex background.A method of military image classification is proposed by combining Local Binary Pattern (LBP) with Pyramid Histogram of Oriented Gradients (PHOG) through Local Discriminant Canonical Correlation Analysis (LDCCA).Firstly, the LBP and PHOG features of military images are extracted, and then the extracted features are fused by LDCCA.Finally, K-nearest neighbor classifier is used to classify the military images.The advantage of this method is that the fused LBP and PHOG features are robust and able to classify images.The results on a data set of military targets show that this method is effective and feasible, which provides a technical reference for the military target identification system.

In order to describe uncertain information more completely, this paper combines triangular fuzzy numbers with Pythagorean hesitant fuzzy sets, and proposes the concept of Pythagorean Hesitant Triangular Fuzzy Sets (PHTFS).In view of the association between data attributes in the process of information integration, this paper extends the Heronian mean operator and the Muirhead mean operator to the PHTFS, and proposes the Pythagorean Hesitant Triangular Fuzzy Heronian Mean (PHTFHM) operator and the Pythagorean Hesitant Triangular Fuzzy Muirhead Mean (PHTFMM) operator.In view of different levels of importance of input variables of different attributes, their weighted forms are presented.Finally, as for the multi-attribute decision-making problem in the Pythagorean hesitant triangular fuzzy environment, a multi-attribute decision-making method based on the Pythagorean Hesitant Triangular Fuzzy Weighted Muirhead Mean (PHTFWMM) operator is proposed, and the effectiveness of this method is illustrated by an example.

An improved A* algorithm for path planning of mobile robots is proposed.First of all,based on the traditional A* algorithm, JPS algorithm is used to extend the child nodes and select the jumping point, so as to improve the efficiency of A* algorithm.Secondly, Bezier curve is used to smooth the path generated by A* algorithm.Finally, based on Matlab, the improved A* algorithm was simulated on 9 groups of different grid maps.The results show that the improved A* algorithm using JPS algorithm and Bezier curve greatly reduces the amount of computation, improves the steering smoothness,and enhances the path planning efficiency with the increase of scale of the surrounding environment map.The improved A* algorithm obviously has better path planning capability than the original algorithm.

This paper studies the derivation and implementation of an algorithm for estimating the angular rate of Line of Sight (LOS) angle between guided munitions and targets.The measurement information of a low-cost strapdown seeker includes two parts, namely, attitude of the missile and LOS angle, which are formed in relative motion between the missile and the target.Firstly, a decoupling algorithm is designed to remove the attitude information of the missile.Then, Extended Kalman Filter (EKF) and α-β filter are used to estimate LOS angle and angular rate respectively.The final simulation results show that EKF can obtain a more accurate estimated value than α-β filter.

In complex scenes, illumination variation and changes in the target′s scale may lead to loss of targets or mis-tracking.To solve the problem, this paper proposes a structured multi-target tracking algorithm adaptive to scale and illumination changes.Multi-scale Retinex algorithm is used to preprocess the image sequence.Structure Preserving Object Tracking (SPOT) algorithm is used to track multiple targets, so as to determine the optimal position of each target in the new frame.Discriminant Scale Space Tracking (DSST) algorithm is used to train the scale filter, whose maximum value is used to determine the optimal scale of each target in the new frame by taking the optimal position of each target in the new frame as the center.Stochastic Gradient Descent (SGD) and bilinear interpolation are used to update the weight of the feature classifier.The experimental results show that the proposed multi-target tracking algorithm has good robustness and accuracy in dealing with illumination variation and changes in the target′s scale.

At present, various chaos-based image encryption algorithms have been designed and developed for different scenarios, but there are still shortcomings of some of the image encryption algorithms.Therefore, relevant experts and scholars continue to study new image encryption methods and improve traditional encryption algorithms.In order to overcome the obvious shortcomings of the current low-dimensional chaotic algorithm, this paper presents an image encryption algorithm based on the combination of CNN hyperchaotic sequence and S-box.Simulation experiments show that：1) The proposed algorithm can effectively resist the attack on the image in the form of plain text or cipher text, realize one-time one-encryption, and have a larger key space; and 2) This algorithm has excellent encryption effects with high speed and low complexity.

The enemy′s netted radar system often jointly estimates the parameters of the detected targets.In order to supress its parameter estimation performance, a deception jamming strategy against the enemy′s netted radar is proposed.Firstly, analysis is made to the principle of deception jamming against the enemy′s netted radar, which is conducted by an electronic jammer formation, and then the target′s echo signal model under the deception jamming is established.Based on this, the Fisher Information Matrix (FIM) of feature vector parameters of deception jamming is presented and then the estimation performance is analyzed.Finally, the sufficient condition of reaching the Cramer-Rao Lower Bound (CRLB) of the parameter to be estimated is derived, and a deception jamming strategy for supressing the parameter estimation performance of the enemy′s netted radar is given.Simulation results have proved the feasibility and validity of the proposed strategy.

The current graph-based multiple kernel clustering usually adopts a linearly weighted strategy, which limits the representational capacity of the consensus kernel and ignores noise pollution in Reproducing Kernel Hilbert Space (RKHS).To solve the problem, a Robust Self-weighted Multiple Kernel Learning (RSMKL) graph-based subspace clustering algorithm is proposed, which is aimed at enhancing the representational capacity of the kernel and improving the robustness to noise in RKHS.This algorithm adopts a novel nonlinear self-weighted kernel fusion strategy to generate the optimal consensus kernel, and then uses Low-Rank Representation (LRR) in RKHS to remove the influence of noise on the quality of affinity graphs.Finally, an alternating direction method of multipliers with iterative optimization is proposed to solve the objective function.The experimental results on five common data sets show that, compared with five popular homogeneous algorithms, RSMKL possesses better clustering performance on the indexes of ACC, NMI and Purity.

To solve the problem that the velocity of Reciprocal Velocity Obstacle (RVO) avoidance is optional in theory but is inaccessible in practical application, after comprehensively considering the advantages and disadvantages of RVO algorithm and Dynamic Window Approach (DWA), this paper fuses the two algorithms, so as to add kinematic constraints to the optional velocity of obstacle avoidance. Furthermore, when multiple mobile robots are encountering in complex environment, the conventional expansion radius may lead to failure in obstacle avoidance. To solve the problem, an Adaptive Reciprocal Velocity Obstacle (ARVO) algorithm with variable expansion radius is proposed. ARVO algorithm can adjust the expansion radius according to the complexity of the environment, which not only ensures a safe distance between the mobile robot and the surrounding obstacles, but also increases the range of optional speed. Finally, the performance of the algorithm is verified by Gazebo simulation platform in Robot Operating System (ROS).

It is difficult to detect small targets on the sea surface due to large amount of calculation and low detection efficiency of the traditional W-H algorithm.To solve the problem, a new small target detection algorithm based on time-frequency ridge of rearranged spectrum is proposed.This paper studies the characteristics of energy distribution of the time-frequency spectrum of the measured data, compares the analysis results of multiple groups of measured data of the traditional algorithm with that of the new algorithm under different polarization conditions, and studies the characteristics of spikes in Hough parameter domain.The algorithm can effectively detect small targets, which verifies the feasibility of the algorithm.Finally, it is concluded that, by selecting the rearrangement algorithm and extracting the ridge of the rearranged time-frequency spectrum, the new algorithm improves the detection ability, reduces the computation amount, and effectively detects small targets on the sea surface with better performance under the condition of HH polarization.

To solve the technical problem of excessive accumulation of errors of camera parameters during aerial triangulation self-calibration of strip sequence images captured by a UAV, a Classified Self-Calibration (CSC) method is proposed.Firstly, based on the GPS information of the images, a KD-tree is set up, and K-Means is used for automatic classification of the images.Then, the self-calibration bundle adjustment of each type of images is conducted respectively, and weighted average is made to several groups of camera parameters obtained through self-calibration.Finally, the global self-calibration bundle adjustment is conducted.Multiple sets of experiment show that, the RMSE of the parameter difference between the CSC method and the calibration method conducted in an indoor calibration field after image point distortion removal is 0.5 pixels, and the RMSE of the checkpoint position is 10.1 cm.Compared with that of Smart3D, VisualSFM and COLMAP software, the results of aerial triangulation of the proposed method could more accurately represent the original pose of the data.In conclusion, the CSC method provides an effective scheme for the aerial triangulation self-calibration of strip sequence images captured by a UAV, which has high practical application value.

When adopting a single-layer strategy of environment exploration, the autonomous map building of a mobile robot in an unknown indoor environment is inefficient.To solve the problem, a composite detector of frontier points is proposed, which relies on the synchronous operation of the global detector and the local detector, as well as the cascading of the filters.This paper adopts a multi-layer strategy of fast environment exploration, which is composed of global detection and local detection, and combines the filtering of candidate frontier points with the comprehensive evaluation of value of the frontier points, so as to determine the most valuable frontier point in the exploration.The data processing method which fuses the dimension reduction of point cloud data with the controller of environment exploration is given.An experimental system of the autonomous environment exploration and map building of a mobile robot has been developed, and the experimental results show that, based on the proposed method, the robot can track the most valuable frontier point in the autonomous environment exploration and SLAM (Simultaneous Localization and Mapping), which improves the efficiency of environment exploration and map building.

In the field of image enhancement, the method of Intuitionistic Fuzzy Sets (IFS) can′t improve the contrast of the image, and the method of Histogram Equalization (HE) may cause over enhancement and loss of detailed information.To solve the problems, an image enhancement algorithm based on IFS and HE is proposed.The image to be enhanced contains a lot of uncertain information, so a new IFS-based algorithm of image enhancement is proposed.This algorithm is realized by synthesizing the membership and non-membership of pixels in an IFS, which can significantly enhance the detailed information of the image, but cannot improve the contrast of the image.Therefore, the IFS-based algorithm is combined with HE, in which IFS-based enhancement is given priority and the defects of HE-based enhancement are suppressed.The algorithm can not only enhance the details of the image, but also improve the contrast of the image.Finally, the effectiveness and reliability of the algorithm are verified by a typical example of image enhancement.

The study focuses on the verification of the equipment maintainability indexes in small-sample tests with multi-source pre-test information.For the situation when the pre-test information comes from different sources, an equipment maintainability verification method in small-sample conditions under the mixed pre-test distribution is proposed.The pre-test distribution of each pre-test information is determined by using Bootstrap method.The weighted credibility fusion method based on the quality factors is improved, and the mean credibility weight factor and the sample-size credibility weight factor are defined.The weight calculation results are more scientific and reasonable.A Bayes sequential probability ratio method under the mixed pre-test distribution is derived.The MTTR is verified through the maintainability verification test of an armored equipment.The results show that the proposed method can effectively verify whether the indexes of equipment maintainability can meet the design requirements.

In order to realize sparse arraying of MIMO radars, this paper presents an optimized design method for MIMO radar array pattern based on Ant Lion Optimizer (ALO).First, the aperture of the transmitting elements and the receiving elements as well as the minimum distance between them are defined.Then, the ant lion algorithm is used to optimize the position of the elements, the fitness function is calculated and compared with the global optimal solution to obtain and save the optimal value, so as to minimize the peak sidelobe level.The number of effective elements and peak sidelobe are taken as multi-objective optimization functions, which are then transformed into single objective optimization by normalization.The results of simulation show that, compared with the existing algorithms, the proposed algorithm has better sidelobe optimization effects and is not prone to fall into precocity.It can also realize multi-objective joint optimization and improve the performance and degree-of-freedom of the radar.

In the field of auxiliary airborne navigation, the current Lidar-based point-cloud terrain segmentation method has low real-time performance and poor adaptability to different terrains.To solve the problem, this paper proposes an adaptive point-cloud terrain segmentation method, which combines the CSF algorithm with the fitting of moving surfaces.This method calculates the hardness value of the cloth through the gridding and recognition of terrains to adaptively segment the point clouds, and then uses the cloth model established in the segmentation process to generate terrain grids for helmet-mounted display.Experimental results show that, compared with the classic terrain segmentation methods released by ISPRS, this method has the advantages of strong adaptability to different terrains and high real-time performance on the basis of ensuring certain accuracy, which has certain value in the application of the Lidar to auxiliary helicopter navigation.

This paper focuses on fault detection when there is time-varying delay, packet loss, and nonlinear sensor sectors in nonlinear network control systems.In order to solve the problem of network resource waste caused by time-triggered periodic sampling, an event-triggered mechanism is proposed, in which the sampled data needs to be transmitted only when a certain triggering condition is satisfied.Based on Lyapunov stability theory and Linear Matrix Inequality (LMI), the fault detection problem is transformed into an H∞ convex optimization problem.Finally, simulation of the motor servo control system is conducted through Matlab, and the validity and practicability of the design are proved.

FC network is widely used in the integrated core processing platform to realize high-speed information transmission between internal and external subsystems.In order to verify whether the FC network can meet the requirements of the avionics system on function, performance and interface definition, the FC network needs to be simulated and verified.An integrated simulation and verification system of FC network is proposed to fully simulate the application environment of the core processing platform and provide a platform-level environment for simulation, verification, testing, monitoring and analysis of the FC network on the core processing platform, so as to ensure that the function, performance and interface definition of the FC network on the core processing platform can meet systematical requirements.Currently, the simulation and verification system of FC network has been successfully verified, which has broad application prospects.

In view of the complexity of aircraft′s electromechanical system, it is of great significance to analyze and model all kinds of faults and study their excitation method.Based on the historical data of flight tests, this paper studies the fuel system in the typical electromechanical system of an aircraft.Aiming at the specific structure and characteristics of the electromechanical system of the aircraft, the system′s simulation model is established by using Matlab/Simulink software, and simulation is designed for a variety of typical faults.Finally, a visualized simulation platform of fault excitation is built by using Simulink and VC++.The simulation results show that the platform can complete the excitation of a variety of faults in the electromechanical system, which meets the expected requirements.

The image method is one of the common methods for detecting LOS stabilization accuracy of an airborne electro-optical pod, which is simple in theory and versatile with low cost.A carrier simulator is used to impose external disturbances on the electro-optical pod, and detect the offset of the fixed target image captured by a load camera, so as to obtain the stabilization accuracy of the electro-optical pod.Above all, the detection of target image offset is the key to the whole process.This paper proposes a method for detecting the target image offset based on the image′s grayscale, which can effectively improve the detection accuracy of the optical axis′s boresight stability.Through theoretical derivation and experiments, it is proved that the detection accuracy of target image offset is above 1/100 pixel,which has high value in engineering application.

This paper studies the optimal underwater depth of the sonar, which is released from an anti-submarine helicopter into shallow sea.The operating distance of the sonar in active working mode is not only related to the sound speed profile of the sea area and the performance of the equipment,but also related to the underwater depth of the sonar.Firstly, BELLHOP model is used to simulate and analyze the acoustic propagation characteristics under three conditions:thermocline sound speed profile, positive sound speed profile and negative sound speed profile.Secondly, the probability distribution of a submarine sailing at different depths under different sound speed profiles is studied, and the model of submarine motion law is established and analyzed.The results show that the probability distribution of a submarine sailing at different depths is different under different sound speed profiles.Finally, by using the probability distribution of a submarine sailing at different depths, the average detection distance of the sonar at different underwater depth is calculated to obtain the optimal underwater depth of the sonar under different sound speed profiles, which can give full play to the performance of sonar equipment.