Current radar net coverage calculation has such deficiencies as huge calculation amount, low velocity and low precision.To solve the problems, the radar models of vertical coverage of three frequency bands are built, and the planar coverage model in geodetic coordinates is built by using the longitude incision method.The detection area of the radar net is incised into a couple of strips with the same longitudinal intervals so that a strip-shaped radar net is formed.The planar coverage of each radar in the net is projected onto the strip-shaped radar net.The strip-shaped model of radar net coverage is built, and an incision algorithm is given.A simulation was carried out on the radar net composed of 30 radars.The result shows that the proposed method reduces the two-dimension calculation amount to one dimension, which lowers the calculation amount, improves the velocity, solves the complex problem of masking boundary, acquires a more accurate spherical area, and solves the problem of the calculation of radar net coverage.

In order to study the random and uncertainty factors in task planning of multi-platform sensor optimization, the Hidden Markov Model (HMM) modeling theory and the information entropy theory were used to support the unit loss of the task.The information gain brought by the unit loss of the platform sensor was taken as the target function.The dynamic programming model of the sensor based on the multi-HMM process was established, and the steps of dynamic programming for the multi-sensor task planning problem were set and simulated.At the same time, we also discussed the dynamic scheduling of the multiple sensors in special cases using the multi-HMM process, which lays the foundation for the modeling and analysis of the uncertainty and stochastic factors for the multi-platform sensor scheduling optimization problem.

To improve the reliability of the highly-sensitive photoelectric detection system in the background of strong radiation, the calculation method of photoelectric imaging detection performance in the background of strong radiation is studied.The sensitivity model and SNR model of photoelectric detection are built based on the optical radiation characteristics of the target, and the calculation function of the charge crosstalk threshold is given.On this basis, the probabilistic models of photoelectric detection and imaging crosstalk are derived.A computational verification and an analysis are carried out.The results show that using an effective method of signal processing and spectral filtering can enhance the detection performance of the system.The target detection probability is increased from 0.3 to 0.95 and the crosstalk probability can be reduced to less than 0.05 by setting reasonable optical system parameters.

There are two modes for air-to-air missile's all-aspect attack: target-certered all-aspect attack and all-aspect attack of the launch platform. The relationship between the two modes are studied from five aspects of concept, representation of attack envelope, calculation of typical attack ranges, display in cockpit, and key technical problems. It is concluded that: 1) The target-certered all-aspect attack pays more attention on the relation of the missile with the target and is the beginning of attacking, while the all-aspect attack of the launch platform pays more attention on the relation of the launch platform with the target and is the ending of attacking; and 2) The target-certered all-aspect attack is the basis for studying the all-aspect attack of the launch platform, but the methods and contents of study are quite different due to the different tools used.

To guarantee the navigation accuracy of integrated MIMUs/GPS during the period of loss of GPS signal, the Adaboost optimized BP neural networks are introduced in the normal Kalman filter.When GPS signal is unavailable, the trained neural networks are adopted to predict the velocity difference & position difference between outputs of GPS & MIMUs at the same moments, which then will be transmitted into the observer of Kalman filter.Specifically, by improving the navigation strategy on the systematic level, the error of navigation parameters accumulated from MIMUs under single mode is corrected.Simulation results indicate that: 1) When losing the GPS signal, the Adaboost optimized BP neural network can help the filter for information processing and compensate for the lost information in 50 s; and 2) Compared with only applying the normal BP or RBF neural networks, better stability and precision of navigation parameter prediction are obtained.

In the infrared target detection methods based on temperature difference, the final target detection result is greatly influenced by the segmentation effect of the infrared image.As to infrared images with certain detection distance, the traditional segmentation method is not fit for the segmentation of the target edge.To solve the problem, Convolutional Neural Network (CNN) is used to extract image features.A spectral-clustering infrared image segmentation method based on CNN is proposed.Experimental results show that this method can improve the segmentation accuracy of infrared images.Compared with that of the traditional image segmentation method based on sparse matrix and spectral clustering, the clustering speed of the CNN-based spectral-clustering infrared image segmentation method is increased by about 16 times.Under certain detection distance, the improved spectral-clustering infrared image segmentation method can acquire more accurate results of the temperature difference between the target and the background.

The robots working indoors must be able to effectively identify their surroundings to complete the autonomous navigation in different scenes.Traditional approaches realize scene recognition by using visual or radar sensors to match the scene.A method of indoor scene recognition based on 2-D range scanning is proposed.This method extracts the features of range scanning information of the lidar, and Extreme Learning Machine Based on Local Receptive Fields (ELM-LRF) is trained by using extracted samples to classify and identify various indoor scenes.In the simulation environment built by Gazebo, the virtual range scanning data is collected, and then the indoor scene recognition methods are studied.The proposed method is verified by experiments based on the range data provided by DR Dataset.The results show that the recognition accuracy of the proposed method is higher than that of traditional methods.The study of scene recognition based on 2-D range scanning also provides theoretical support and experimental data for autonomous robot navigation.

To solve the problems in the positioning and orientation technology of the missile launching vehicle in the background of independent emission, a scheme of integrated navigation of SINS/odometer (OD) based on Radio-Frequency Identification (RFID) is presented.A variety of positioning approaches based on RFID for the launching vehicle are designed and the related problems in actual use are analyzed.Then the scheme is applied to the mature SINS/OD mode.The model of SINS/OD/RFID is built, and the approaches to the auxiliary positioning in progressing are explored, which provide a new auxiliary method for the positioning and orientation development of the launching vehicle.The experimental results of this scheme show that, the positioning accuracy can reach the level of meter, which can effectively extend the navigation time meeting the precision requirements, and improve the application performance.

In order to integrate the performance evaluation of UAV cooperative combat with the actual combat, the prediction of UAV cooperative combat process is studied. The characteristics of the UAV's mission in cooperative combat for ground reconnaissance and attacking are analyzed, and the Lanchester equation of UAV cooperative combat is designed. The Lanchester equations of UAV cooperative operation based on force reinforcement and under different payloads are designed.The simulation experiment proves the rationality and feasibility of the designed Lanchester equations for UAV cooperative operation.

The Kernelized Correlation Filtering (KCF) tracking algorithm has no occlusion detection mechanism, and has a fixed learning rate.To solve the problems, a KCF tracking algorithm combined with the positive sample set is proposed.The mechanism determining target occlusion is set up by calculating the similarity between the positive sample set and the sample set to be tested, and thus the anti-occlusion ability of the algorithm is improved.As to parameter updating, the method of multi-step learning rate is adopted, which improves the accuracy of the target model.Experimental results show that, compared with that of the KCF tracking algorithm, the tracking accuracy of the proposed method is obviously improved.

For textureless objects with smooth surfaces in vision tasks, conventional three-dimensional reconstruction methods cannot retrieve the valid shape information of surfaces due to the loss of structure-texture features and the high reflectance.This paper presents a 3D reconstruction algorithm for textureless objects based on optical polarization imaging, which does not rely on the structure-texture features of objects.Stokes vector is calculated to quantify the polarization states of the surface reflected light.Then, based on the analysis of polarimetric-geometric space, the normal distribution of surface is effectively estimated.Finally, the multi-scale Shapelets blank operator is applied to integrate the normal vector to derive the accurate depth information of the objects.Experiments show that the proposed method can obtain the correct 3D shape reconstruction results, especially for high-reflective objects with less texture features.Meanwhile, the proposed technique can suppress the specular highlight and noise, with high computational efficiency.

Quadrotor UAVs have been widely used in various fields.Flight control algorithms are the control center of the UAV, which determine the flying state and achievable maneuvers of the UAVs.Scholars at home and abroad have made abundant accomplishments by their intensive study about the controllers of quadrotors.This paper systematically summarizes and evaluates the research findings of flight control algorithms in recent years, mainly introduces and briefly evaluates their fundamental design principles, points out the strengths and weaknesses of each algorithm and discusses their research prospects.The purpose is to provide some reference for the research in the area of flight control of quadrotors.

Prognostics and Health Management (PHM) technology of the avionics system can greatly improve the safety, reliability and stability of aircrafts, so as to reduce the maintenance costs. The structure and management functions of Integrated Modular Avionics (IMA) system are briefly introduced.Based on ASAAC Standard and OSA-CBM model, the design of PHM for IMA system is analyzed from the aspects of the structure, function layer and tree-form Run-Time Blueprint(RTBP).Finally, according to the characteristics of avionics system, the application and development of future avionic PHM technology are discussed.

Satellite identification and tracking system is the key technology in the control system of Satcom on the Move(STOM).The scheme adopted by the current satellite identification and tracking system is studied.According to the actual working requirements of STOM system, a dual-source scheme for the satellite identification and tracking system is proposed.Based on the scheme, the hardware and software design of the dual-source satellite identification and tracking system is completed.The scheme can detect the changes of satellite signal intensity in real time with fast response speed and high tracking accuracy, so that the system can identify and track the satellite by detecting particular signals and the changes of signal intensity, and keep the communication link smooth.In the phase of detecting beacon identification satellite, an FFT algorithm of correcting frequency deviation is adopted, which improves the accuracy and stability of the system.

The single photon counting technique is an effective method for detecting weak optical signals by using a photomultiplier tube, which is widely used in lidar detection in the area of aviation.Considering that lidar echo signals have such characteristics as weak strength, large data volume and high real-time requirements, we designed a high-speed data acquisition system based on the FPGA chip.The system accurately detects the weak lidar echo signal by using the photomultiplier tube, and the counting collection without time intervals is realized through the ping-pong operation by using two photon counters.Storage controllers and transmission controllers are used to control the peripheral chips.In this way, the real-time caching and high-speed transmission of the acquired data are realized.In order to verify the effectiveness of the proposed system, it was applied to the mie-scattering micro-pluse lidar and realized an effective detection of 7.5 km with a detection accuracy of 7.5 m.On this basis, a comparison was made to the measurement results of the acquisition system and the EASY-MCS photon counter.The mean square error was 8%.It indicates that the proposed system has high accuracy and stability, and can meet the requirements of weak echo signal acquisition of the lidar, which will have broad application prospects in the field of aviation meteorological detection.

A piecewise compensation scheme is proposed to solve the problem that the digital closed-loop fiber-optical gyroscope is sensitive to changes of ambient temperature in its working process, which may lead to temperature drift.By analyzing the causes of temperature drift, a multi-model fitting method is introduced based on a new segmentation strategy and the traditional single-model fitting method.In this method, the full-range test data is divided into several data areas, and the parameters in each interval are estimated through multiple linear regression.In its realization process, the microcontroller collects the temperature signals in the center of the fiber-optical ring in every second, which are used for data processing.The compensation amount is calculated by using the model parameters from corresponding segments.The FPGA reads the compensation data for temperature compensation.Experimental results show that this scheme has better compensation effects than that of the method of single model fitting,and the full temperature error of the compensated gyro is reduced by an order of magnitude, compared with that of the uncompensated gyro.

The movement of a double-frame aircraft skin inspection robot on the outer surface of the aircraft is discussed. Firstly, the movements of the robot are analyzed and divided into two types of the sliding gait and the rotational gait.Secondly, the single-step motion of the robot is regarded as the coupling of the sliding and rotational motion according to its mechanical structure.Then, in view of the single-step motion, a kinematic model of the robot is established, which includes a position dynamics model based on Cartesian coordinates system and a kinetic model based on the coupling of the rotation angle and the sliding displacement. Finally, in order to accomplish the robot's tracking movement on the outer surface of the aircraft, a sliding mode controller based on RBF adaptive compensation is designed.By using Lyapunov stability theory, the convergence of the tracking error is proved.Simulation results have verified that the aircraft skin inspection robot can realize the asymptotic tracking of the desired trajectory, and has a satisfying control performance.

The traditional method of fuzzy comprehensive assessment has the problem that the evaluation result is not well-determined because the choosing of the membership function is too subjective.A qualitative and quantitative conceptual conversion model, named the cloud model, is introduced, and a fuzzy comprehensive assessment method based on the cloud model is proposed.First, by introducing the cloud model to replace the single membership function in the original fuzzy comprehensive assessment method, the randomness and ambiguity of the qualitative results are characterized.Secondly, in the evaluation process, the problem of absolute boundary is avoided by improving the division of the evaluation scope.At the same time, a new quantification interval is determined based on the cross quantification interval, and the cloud model is obtained accordingly to avoid the computing of the reverse cloud.Finally, the evaluation performance of the three methods of the cloud-model comprehensive evaluation method, the original fuzzy comprehensive evaluation method and the proposed algorithm is analyzed by evaluating the example of flight avoidance safety, and the validity of the proposed method is verified.

Traditional ways to integrate airborne weapons have such shortcomings as long integrating cycle and high costs.To solve the problem, the concept of a new plug-and-play weapon integration technology based on configuration technologies was studied.In order to decouple weapon support service used to describe weapon-specific behavior and the integration framework, a type of service-oriented and open framework of weapon integration was proposed.The parameter configuration file with standard format and the program configuration file that has standardized store control APIs and store OSI APIs were designed to define weapon-specific behavior.Then the structure of storage directory for the configuration file system was illustrated.Analysis and validation have proved that it is feasible to realize the rapid integration of plug-and-play weapons by using configuration technologies.

To solve the problems of large volume and chromatic aberration in the distributed-aperture optical system working in multi-waveband environment, a refractive optical system is proposed as an alternative, in which the visible light and Long Wave Infrared(LWIR) share the mutual aperture and prism splitter.The mutual optical path of different wavebands is realized by utilizing certain kinds of materials that both visible light and LWIR can pass through.The optical system adopts the passive athermalization design.The design result suggests that, for the visible light working in the range of 0.55~0.75 μm and LWIR working in the range of 7.5~9.5 μm, their focal length is 120 mm and the F number is 2.8.The MTF for visible light is above 0.5 at a spatial frequency of 90 lp/mm, while the MTF for LWIR is above 0.4 at a spatial frequency of 17 lp/mm.Athermalization is realized in the temperature range of -40~+60℃.

A method for the simultaneous estimation of the range and velocity of moving targets based on the single dechirped wideband echo is researched.After accurate analysis and derivation, it is indicated that the dechirped wideband echo of a moving target is a kind of LFM signal, whose parameters contain the range and velocity information of the target.Based on this, a method for the parameter estimation of LFM signals based on FRFT is proposed to accurately estimate the range and velocity of the target by using the single wideband echo.This method can not only realize the simultaneous estimation of the range and velocity of a single target or multiple targets, but also double the PRF of the wideband system compared with the working mode switching between wideband and narrowband.This is very beneficial for wideband imaging and target identification.Simulation results indicate that the proposed method not only has low computational complexity and high estimation accuracy of the range and velocity, but also can adapt to situations of different duration and widebands of the signal and different SNRs.It is an adaptable and robust method.

To solve the problem that uncertainties exsiting in the performance degradation data and failure threshold are always ignored in current methods, a new method of reliability analysis of performance degradation products based on blind number and GM(1, 1) model is proposed. Firstly, the blind number of degradation data and failure threshold is defined.The method of gray number ranking is used to optimize the confidence level of the blind number. The degree of reliability at the time of the measurement is calculated through blind number operation. Then, the parameter estimation of three-parameter Weibull distribution is carried out by using the GM(1, 1) modeling method, and the function of reliability degree is obtained. Finally, a case study on the degradation data of GaAs laser is provided to verify the effectiveness of the proposed method.The results illustrate the rationality, validity and high fitting precision of the proposed method, which can be used in engineering applications.