The basic concept of killing zone is presented, which can be divided into the vertical killing zone and the horizontal one. Considering that the missile speed vector can be decomposed into the vertical plane and the horizontal plane, the three-dimensional method of proportional guidance trajectory is introduced, which is then used to simulate the process of missile/target encountering under the guidance of single warship and dual-warship cooperation. The simulation results show that collaborative guidance can greatly improve the far edge of the killing zone of the ship-to-air missile at low altitude, and effectively improve the operational efficiency of the missile.

With the wide application of Unmanned Aerial Vehicles (UAVs), using UAVs for detecting and tracking the ground targets has become a research hotspot. Aiming at the problem of false detection and loss of target during the detection and tracking of moving objects by the optical flow method, the target detection is performed by using the longitudinal edge features of the moving object in conjunction with the optical flow method.Simultaneously, Scale-Invariant Feature Transform (SIFT) and optical flow method are used to realize target tracking. A method of matching the constant-speed model with the optical flow block is proposed to solve the problem of occlusion of the target by buildings during tracking.The experimental results show that the method can implement effective target identification and tracking, and has strong robustness.

Focusing on the two problems existed in Staple tracker, an improved target tracking algorithm based on Staple tracker is proposed. Firstly, in order to strengthen the discriminative ability of Staple tracker for gray video sequences, a histogram classifier based on locality sensitive histogram is proposed. Furthermore, as the constant merge factor in Staple tracker can not merge two classifiers optimally, an adaptive merge factor is proposed by using the relative confident coefficient. Experiment is made on OTB2013 benchmark for comparing the proposed tracker with other 9 state-of-the-art trackers. The results show that the proposed tracker has a precision of 0.814 and a success rate of 0.614, improved by 4.1% and 3.5% respectively compared with that of the Staple tracker, which indicates that the proposed tracker is more robust.

The effective sample partition in the near-infrared spectroscopy model is studied.When classical Kennard Stone (KS) algorithm uses the distance metric to describe the difference between high-dimensional spectral data, the effect is unsatisfactory or even meaningless.To solve the problem, and considering the shortcomings of current similarity measurement methods, we constructed a new similarity measure function.The spectral features and property features were combined to calculate the difference between the samples.An improved KS algorithm was thus proposed to find the best expression of sample difference. The improved algorithm was analyzed from the aspects of effectiveness and the impact on the near-infrared spectroscopy model by comparing with other improved methods, and the rationality and superiority of the proposed algorithm were verified.

The Kernel Principal Component Analysis (KPCA) method in the subspace method was used together with the LVQ neural network (KPCA-LVQ) for radar target one-dimensional range image recognition, which has achieved good recognition results. The study found that the unknown parameters in kernel function are difficult to determine when using KPCA. An in-depth analysis of the relationship between the kernel function matrix and the kernel function parameters revealed that there is a certain correspondence between the contribution rate of the principal component and the parameters of the kernel function. In this regard, an optimization problem based on principal component contribution rate was established, and Particle Swarm Optimization (PSO) algorithm was used for obtaining the optimal solution. The experimental analysis showed that the method overcomes the problem that the unknown parameters in KPCA is determined depending on experience, reduces the calculation cost and improves the target recognition rate.

To the problem of mathematical modeling of taxiing of UAVs with articulated landing gear, a detailed analysis is made to the movement characteristics of the landing gear buffer. Considering the wheel rolling friction characteristics, the main wheel differential braking characteristics and the lateral deflection of the wheel, also taking into account of the aerodynamic force and its torque, and based on the theory of rigid body dynamics and kinematics, we established a six-degree-of-freedom nonlinear full-scale mathematical model, which can fully reflect the physical motion characteristics of the taxiing of UAVs. Taking a certain UAV as an example, we carried out mathematical modeling and simulation analysis. The simulation results show that the established six-degree-of-freedom nonlinear full-scale mathematical model can truly reflect the physical movement characteristics for UAV taxiing.

Traditional resampling methods in particle filter suffer from the disadvantages of unstable filtering performance, severe fluctuation of Effective Sample Size (ESS) and sample impoverishment. A target tracking algorithm based on particle filter with Chopthin resampling is proposed.Compared with traditional resampling, Chopthin resampling produces a set of unequal weighted particles. It discards less edge particles when the particles are relatively concentrated, which is helpful for solving the sample impoverishment problem of the traditional resampling.Different from the traditional resampling that only occurs when ESS is below a threshold, Chopthin resampling can be implemented in every iteration cycle. Therefore, the ESS is and filtering performance are more stable. Simulation shows that the new method can overcome the disadvantage of traditional resampling without increasing the calculation cost.

Situation assessment of the target is the basis for making military decision in anti-submarine warfare. Through analyzing the antagonistic process between the helicopter formation and the target, and considering the insufficiency and uncertainty of target information in the anti-submarine warfare, we proposed a kind of situation assessment algorithm based on Bayesian network. By establishing an association network between the two sides and the relevant causality and probability tables, we used Bayesian network for ratiocination, and thus to identify the target and its intention. The experiment results showed the validity of the algorithm, which can provide a good reference for military decision-making in anti-submarine warfare by using the target information effectively together with the empirical model for ratiocination and identification.

In such environments as forestsindoor rooms and high-altitude orbits with weak signalsthe satellite navigation receiver can not use the traditional Frequency-Locked Loop (FLL) to track the Doppler frequency of received signal normally.To solve this probleman improved algorithm of FLL based on maximum likelihood estimation is proposed.In this algorithmthe maximum likelihood estimation module is inserted before the mixer of the traditional FLL.The proposed algorithm is formulated in detail.Based on the structure of traditional FLLan implementation scheme is also proposed to enhance its performance.Simulation results show that:Compared with the traditional FLLthe improved algorithm has a great improvement on the performance of frequency tracking.

In order to solve the contradiction between the positioning accuracy in the traditional star selection algorithm and the complexity for calculating the Geometric Dilution of Precision (GDOP), an improved fast satellite selection algorithm is proposed. By using the elevation angle and azimuth information of the satellite to divide the region, the satellite with signal-to-noise ratio less than a certain threshold is eliminated, and the GDOP calculation method of Newton's identity is used to finally determine the selection of six satellites for positioning. The optimal satellite geometric distribution region for less period calculation can greatly reduce the amount of computation.Theoretical analysis and experimental results show that:Compared with the minimum GDOP algorithm, the proposed algorithm has a significant reduction on computation amount when the positioning accuracy is slightly sacrificed, and the positioning accuracy is higher than that of the Quasi-Optimal algorithm, which proves the effectiveness of the algorithm.

In view of the real-time requirement of the application of camera position estimation, and of the defect of the RPnP algorithm that it cannot get a unique solution when using the least square error, an adaptive accelerating RPnP algorithm is proposed.When calculating the error, more constraints are added to the least square, and the uniqueness of the output solution is guaranteed. Afterwards, the corresponding camera parameters are calculated, which are used to replace the calculation and comparison process of each minimum reprojection error, and thus can save a lot of time. Finally, the original algorithm and the improved algorithm are adaptively combined to optimize the output results. The experiment shows that this method can greatly reduce the time complexity of the algorithm, and the running time is hardly influenced by the number of points, which is more adaptable to the scene with high real-time requirement.

The temporal correlation characteristics, spatial correlation characteristics and the amplitude statistics of the measured sea-clutter data with the grazing angle from 44° to 69° were studied. Experiments showed that correlation time of sea clutter was maintained in the order of milliseconds, which increased at first and then decreased with the increasing of the grazing angle, and the strongest correlation time appeared at about 60°.At the same time, the average number of correlation distance units increased with the increasing of the grazing angle. Hybrid distribution, a new sea clutter amplitude fitting distribution, was proposed according to the amplitude statistics. Contrast test was made to the new distribution with such distributions as KK distribution, Rayleigh distribution, lognormal distribution and K distribution. The result shows that the new hybrid distribution has fine fitting effect to the sea clutter with large grazing angle, especially to its tailing characteristics.

A mechanical analysis is made to the quad-rotor aircraft, and a mathematical model of 6 degree-of-freedom is established. Then, on the account of the underactuation and strong coupling characteristics of the quad-rotor mathematical model, a control method based on integral backstepping (IBS) and Linear Active Disturbance Rejection Controller (LADRC) is proposed for trajectory tracking and controlling of the quad-rotor aircraft. Firstly, the whole control process is divided into outer-loop position control and inner-loop attitude angle control. Then, for the under-actuated outer-loop position control, the IBS control method adaptable for under-driven system is used; as to the seriously coupled inner-loop attitude angle control, the LADRC control strategy with anti-coupling effect is adopted. The result of experimental simulation shows that the method can realize the trajectory tracking of the quad-rotor aircraft.

In computer vision head pose measurement system, large space is needed for the installation of coded feature points. To solve the problem, a new method is put forward for the measurement of the Line-of-Sight (LOS) with a simple structure as well as smaller installation space. The method uses the positioning cameras to capture the picture of the coded feature points on the helmet, and a computer for image calculating and analyzing. The head pose is obtained finally with the Orthogonal Iterative (OI) algorithm. Based on the characteristic of the feature points, we conducted a series of simulation analysis. The simulation results proved the rationality and the validity of the method, and showed that a high-precision LOS can be obtained by using the coded feature points distributed within a smaller space.

Aiming at the attitude control problem of quad-rotor aircraft with modeling uncertainties and external disturbances, an observer based adaptive sliding mode control algorithm is proposed. Based on the establishment of attitude error dynamics model of quad-rotor aircraft, the unknown state feedback of the system is obtained by the global asymptotic convergence observer, the adaptive sliding mode control is used to suppress the uncertainty and interference of the system. Finally, an adaptive sliding mode attitude, controller based on observer is constructed.The stability analysis based on Lyapunov shows that the tracking error of the method is uniform and ultimately bounded. The results of numerical simulation show that, compared with the existing sliding mode control method, the proposed method has better attitude tracking performance and higher anti-disturbance robustness. It can effectively guarantee the attitude tracking control performance of the aircraft with higher robustness.

The hysteresis nonlinearity of piezoelectric ceramic driving platform affects its positioning accuracy and reduces the quality of such micro-nano manipulation systems as atomic force microscopy for scan imaging of micro/nanoscale samples.The hysteresis characteristics of piezoelectric ceramic driving platform are modeled by the least squares methodthus the cumbersome hysteresis model acquisition process is avoided.The composite control method combining feed-forward control with active disturbance rejection control is proposed.By setting the bandwidths of the expansion observer and the system controlthe purpose of eliminating hysteresis and improving the positioning accuracy of the platform is achieved.The experimental results show that the composite control method can effectively improve the system positioning control accuracy under the premise of ensuring system stability.

An analysis is made to the basic links that have effect on the resolution of the electro-optical reconnaissance system, and a method for establishing general performance predicting model is presented.With the Minimum Resolvable Temperature Difference (MRTD) as the ultimate evaluation index, the model for calculating the resolution of IR electro-optical reconnaissance system is constructed. A specific example is used for numerical simulation, which verifies the applicability of this model.

With the rapid development of unmanned helicopter technology, the unmanned helicopters are used more and more frequently in battlefield. When using the unmanned helicopters to aid the warships for target detection, the detection range can be enlarged greatly, which can supply more comprehensive battlefield information to the commanders. In recent years, Kalman filtering are finding applications in the areas of communication, navigation, guidance and control, and various improved Kalman filtering algorithms have been proposed. In order to improve the detection precision of the unmanned helicopter, a Kalman filtering algorithm based on Monte Carlo method is presented here, and an analysis is made to its precision.

Laser designation distance of electro-optical targeting system is an important design input of the laser designator and the fire control system.By using the energy transfer sequence of laser-guided system, we obtained the energy density at the entrance pupil of the seeker, and analyzed the factors that have effects on the designation distance.The empirical equations of target reflectance factor and atmospheric transmittance were deduced, and the functional relationship between the designation distance and such factors as laser energy, seeker sensitivity threshold, the divergence angle and the size of target, etc.Then the designation distance was obtained with the aid of Matlab. The calculation result showed that:1) The designation distance increases with the laser energy, and the slope decreases obviously when it achieves a certain distance;2) For a surface target, the maximum designation distance is reached when the target normal is parallel to target-seeker direction;and 3) There are some other important factors that have effects on the designation distance, including the attack height, the target size, the seeker sensitivity threshold, the atmospheric parameters, and so on.

The electro-optic system plays more and more important roles onboard military helicopters, including infrared/television imaging, target searching, tracking and monitoring, etc. The method for making ground comparison test on the important function performances of the electro-optic system is presented, which can reduce the economic cost and consumption of manpower and time associated with the flight test.

Aiming at the problem that bias of MEMS gyroscope is severely affected by temperature, a temperature compensation method based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is proposed. Firstly, the smoothed gyroscope data is taken as the sample point, and the drift model is constructed by the SVM method based on radial basis kernel function. The data is mapped from low-dimensional space to high-dimensional space for linear fitting to ensure generalization ability. Then, the PSO algorithm is used to optimize the penalty parameters, kernel function parameters and bias parameters of the SVM, which avoids the blindness of artificial parameter choosing and improves the accuracy of the model. Experimental results show that gyro output accuracy is higher after PSO-adjusted SVM compensation. Compared with the least squares method and the BP neural network method, the variance of the gyro output data is reduced by 81.3% and 57% respectively, and the maximum error is reduced by 54.7% and 48.5% respectively.