Doppler spectrum is the frequency-domain performance of the time-varying characteristics of the channel. In order to study the time-varying characteristics of the channel in the reverberation chamber, an approximate model of Doppler spectrum in the reverberation chamber is built by analyzing the scattering mechanism and the distribution of scatters, and experiments are designed to validate the model. The experimental results show that the model can describe the Doppler spectrum in the reverberation chamber, and the electromagnetic environment with certain Doppler spread can be synthesized in the reverberation chamber by selecting the appropriate stirring velocity and carrier frequency, which is of great significance for describing and controlling the time-varying characteristics of the channel in the reverberation chamber.

In the process of radar signal detection, the entire-section pulse compression detection of the long-period signal will be time-consuming and take up too many resources. A segment matching algorithm is used to achieve local pulse compression detection. First, the received echo signal is segmented according to certain rules, and the sliding window detection is carried out in each segment after filtering the data segment by segment, and then the result of the first judgment is saved after data screening. After a number of pulse cycles, the preserved data is aligned in rows. The second judgment is obtained after fine processing, and the target results are ultimately determined. Simulation and test results show that, compared with the traditional method of first compression and then accumulation detection, this method can effectively filter out lots of useless information in processing and save resources, and it is easy to implement.

A parameter setting method is proposed for the nonlinear extended state observer in the Active Disturbance-Rejection Controller (ADRC) by using Back-Propagation Neural Network (BPNN). This method simplifies the complicated parameter setting process of the nonlinear extended state observer. Simulation is made to the BPNN based ADRC system. The simulation results show that this method can significantly enhance the disturbance isolation of the airborne optoelectronic stabilized platform, and the control effect is obviously superior to that of the traditional active disturbance-rejection control method, which is of great significance for improving the optical-axis stabilizing accuracy of the airborne optoelectronic stabilized platform.

Considering that in the naval battlefieldthe information detected by the multiple sensors of the collaborative detection system are uncertainincomplete and even in conflictwe used the DSmH fusion rules and Proportional Conflict Redistribution (PCR) rules to solve the conflicting and offsetting problem of the evidence sources.Comparison was made to different fusion rules by simulationin order to provide a theoretical support for carrier aircraft fleet to implement multi-sensor synthetic target identification.

In Laser Scanning Display System (LSDS) based on Lissajous pattern, the reconstruction of laser-modulated pixel data stream becomes the key point for the implementation of the system due to the difference between Lissajous scanning pattern and the traditional raster scanning pattern. This paper proposes two methods for laser modulated pixel data stream reconstruction, namely, the pixel-based method and the method of the equal interval sampling of the scanning line. The uniformly-spaced sampling method is easy to implement while the pixel-based method can correct the projective distortion of the system by changing the look-up table. The results of the theoretical analysis are proved by simulation and engineering implementation.

Aiming at the fusing characteristics of Synthetic Aperture Radar (SAR) images and optical images, an algorithm for image fusion based on Non-subsampled Contourlet Transformation (NSCT) is proposed. The SAR images and optical source images are decomposed by NSCT transformation. To the low-frequency sub-images, the local entropy is taken as the measure parameter to carry out neighborhood fusion. To the top layer of the high-frequency sub-images, the fusion method of “choosing the biggest” for the regional standard deviation is adopted. As to the other layers of the high-frequency sub-images, the neighborhood correlation coefficient is taken as the threshold, and the neighborhood algorithm based on average gradient selection is adopted to carry out the fusion. Finally, NSCT inverse transformation is utilized to obtain the final fusion image. The experimental results show that the ideal fusion image and more detailed information can be obtained by using this method.

In the Direction of Arrival (DOA) estimation of uniform linear array, the number of the sources that can be estimated is inadequate. To solve the problem, a DOA estimation algorithm based on virtual aperture expansion is presented. Firstly, vectorization processing is made to the received signal covariance matrix of non-uniform sparse matrix, and a new covariance matrix is obtained by Khatri-Rao integral operation. Then, the rank of new covariance matrix is restored by using spatial smoothing algorithm for arbitrary array. Finally, the DOA estimation is implemented by the eigenvalue decomposition of the covariance matrix. Compared with the traditional MUSIC algorithm, the DOA estimation can implement DOA estimation under the condition that the number of array elements is less than the source number, and can greatly increase the estimated source number. Meanwhile, it can obtain DOA estimation results under low SNR and small snapshot conditions. Simulation results demonstrate the effectiveness of the proposed algorithm.

To solve the internal attack problem of the wireless sensor network, this paper proposes a credibility-based trust model by using the existing classical credibility mechanism. The model consists of three levels: credibility calculation, trust management and trust decision-making. Credibility calculation is the core of this model. The direct credibility, indirect credibility and energy credibility are combined to form a comprehensive credibility. The network energy consumption is reduced by updating the credibility regularly. The credibility threshold is set to make trust decisions, and effectively distinguish malicious nodes and aging nodes, thus to ensure the security and reliability of the network. The simulation results show that the model can detect malicious nodes effectively, has a high detection rate, and greatly reduces the energy consumption of the whole network.

Aiming at the need for automatic identification of haze concentration in the adaptive dehazing system, this paper presents an algorithm for haze image classification based on Support Vector Machine (SVM) and mixed feature. On the basis of the characteristics of the haze image, the mixed feature vector composed of the dark channel feature, the wavelet feature and the Mean Subtracted Contrast Normalized (MSCN) feature is adopted to describe the characteristic differences of the images with different haze concentration. The SVM classifier implements the automatic identification and classification of haze images through supervised learning of the mixed feature vectors. Experimental results show that the method can effectively identify the images of haze-free, thin haze and dense haze, which provides a good basis for the dehazing system to select dehazing parameters adaptively based on the haze concentration.

To increase the amount of image information after fusion and improve image clarity, an improved image fusion algorithm based on the Red-Black wavelet is put forward. The PCA transform is performed to the source image before Red-Black wavelet transform. Then the multi-scale Red-Black wavelet decomposition is carried out to the obtained first component. For the high-frequency and low-frequency sub-band coefficient, the selection based on the maximum value and the fusion rule based on the regional variance are adopted respectively. The Red-Black reconstruction is carried out to the sub-images of each level after the fusion, and PCA reverse transform is carried out to the reconstructed results to obtain the fused images. The simulation results show that, the multi-spectral image fusion performed by the proposed algorithm can effectively preserve spectral information, has a high spatial resolution, improve the entropy, increase the amount of information of the fused images, and improve image clarity effectively with better visual effects. It has a wide application prospect in the field of remote-sensing image processing.

Based on the application scenario of the airborne stores management system and the aerospace standard AS5653, the topological structure, network management and the communication scheduling mechanism of the high-speed Fiber Channel (FC) network are explored. The implementation plan is put forward from three aspects:the control and management of node initialization, the scheduling and management of network communications, and the monitoring and management of the effective node activities. This technique provides a feasible technological model for the next-generation airborne stores management system to construct a high-speed network by adopting FC.

n order to solve the problem of low detection probability in the traditional spectrum sensing algorithm under low SNR environment, a clustering cooperative spectrum sensing algorithm based on improved double-threshold energy detection was proposed. The algorithm divided the sensing nodes into clusters. “OR” rule was used to the information between the clusters for hard fusion. Inner the clusters, to the nodes outside dual-threshold, a 1 bit decision result was transmitted for hard fusion; and to the nodes inside dual-threshold, energy values and SNRs were transmitted to implement weighted soft fusion. The detection probability and the node weight coefficient function were constructed under weighted soft fusion, and particle swarm optimization algorithm with compression factor was used to further optimize the function and maximize the detection probability. The simulation results verified that the algorithm has good detection probability under low SNRs and different number of users.

The design of trajectory tracking controller for a quad-rotor Unmanned Aerial Vehicles (UAV) is difficult due to the UAV's inherent characteristics of under-actuation and strong coupling. Considering the model uncertainties and external disturbances, we designed a trajectory tracking controller for the quad-rotor UAVs with timing-varying mass and wind disturbance based on the idea of the control structure of the inner-outer loop. An adaptive sliding-mode controller was designed, which implemented the trajectory tracking in the outer loop and generated the expected lift force and attitude angle. The active disturbance-rejection control technique was adopted to design the inner-loop controller, which was used to follow the expected attitude angle. The simulation results of trajectory tracking in the presence of external disturbances prove the effectiveness and robustness of the designed controller.

Aiming at the observed degradation data of the one-dimensional performance of the products, the proportional hazard model is applied to describe the relationship between the product's life span and its degradation failure rate. The proportional hazard degradation model is built to derive the product's reliability function. The Anderson-Darling (AD) test method is used to determine the degradation distribution model. The most common degradation distribution type at the time points of observing is chosen as the basic distribution to obtain its basic failure rate function and the proportional coefficients. An example is given by using the degradation data of a certain laser to verify the effectiveness of the proposed model.

For the random error of inertial devices, a contrastive analysis was made to the applicability and real-time performance of the traditional ARMA modeling method and the popular deep learning LSTM network. A simulation example was designed by aobtaining the output data of a specific inertial device. The study showed that:1) Without the real-time performance requirement, both ARMA modeling and LSTM network can achieve accurate prediction results, and the LSTM network is advantageous since it doesnt need to extract the signal trend and cycle terms;and 2) In real-time prediction, the LSTM network has obvious advantages, but the prediction accuracy decreases with the shortening of time series, whereas the output can still reflect the trend of noise change and can be used for optimizing the filtering algorithm of the whole control system.

Axial system error in the airborne photoelectric turret system is one of the main factors influencing servo tracking and the pointing accuracy of the optical axis. In order to analyze the influence magnitude of the axial system error on the precision, the principle of coordinate transformation was used, and the mathematic models of the airborne photoelectric turret system under the ideal state of yawing and pitching, as well as under servo-tracking with axial system error, were built up. Numerical computation and simulation analysis were carried out. The characteristic curve of the simulation showed that the influence of the axial system error on the precision varies at different servo angles. In order to satisfy the requirements of the airborne photoelectric turret system, such as a high-precision servo-tracking and pointing of the optical axis, and high-resolution environment detecting and sensing, a method for error elimination and precision improvement was put forward. The method can be applied to the design analysis, error control and technical problem disposal of the airborne photoelectric turret system, and is of significance for practical engineering applications.

An infrared dim and small target detection algorithm is proposed based on adaptive Two-Dimensional LMS (TDLMS). Based on the traditional TDLMS algorithm, the improved TDLMS algorithm can evaluate the difference between the background and the template by using template compression and the hollow method, and adjust the iterative step size adaptively to carry out background filtering. And then a differential operation is performed to the background image for target detection. The experimental results show that the improved TDLMS method has a higher detection rate than the traditional TDLMS method.

This paper proposes a reconstruction and recovery strategy in order to improve the reliability of the triplex-redundancy flight control computer with a distributed architecture. Firstly, the redundant resource allocation of the triplex-redundancy flight control computer system is introduced from three aspects of the flight control computer, the sensor and the actuator. Secondly, the reconfiguration strategy of the control unit is studied based on the characteristics of the flight control computer with triple-redundancy control units. Meanwhile, the reconfiguration strategies of sensors, pneumatic rudders, interface units and buses are discussed briefly. The fault recovery of the control unit is also studied. The algorithm of fault recovery is designed, the data critical for recovery is selected, and the corresponding recovery protocol is formulated. Finally, a test is carried out on the UAV simulation platform. The results illustrate that the reconstruction and recovery strategy is not only reasonable and effective, but also can increase the reliability of the triplex-redundancy flight control computer.

Considering the characteristics of the quad-rotor Unmanned Aerial Vehicle (UAV) such as under-actuation, non-linearity, strong coupling and susceptibility to external disturbances, a control method based on fast Terminal Sliding Mode Control (TSMC) is proposed to guarantee the UAV's control quality and precision requirements. The dynamic mathematical model of the quad-rotor UAV in the turbulent wind field is built. The control algorithm of the global fast terminal sliding mode which can automatically eliminate the vibration is used to calculate each control law, and the under-driven coupling control is realized by introducing the control law of transitional input. The system module is constructed and simulated by using Matlab/Simulink software. The simulation results show that the fast TSMC controller algorithm has high reliability, fast convergence speed and strong robustness, which can effectively complete the quad-rotor UAV's flight tasks.

With the increasing of complexity of the battlefield environment and fight tasks, operations conducted by either the helicopter or Unmanned Aerial Vehicle (UAV) solely have its own limitations. In order to realize the mixed-formation operation of helicopters and UAVs, it is necessary to study the helicopter-UAV cooperative test technology, so as to provide a theoretical basis and experimental data support. Based on the cooperative combat tasks of helicopters and UAVs, and taking the verification of the second-stage helicopter-UAV cooperative capability as the objective, we constructed the helicopter-UAV relay communication profile. From the top to the bottom, the profile was decomposed by tasks and test points. The key technologies during the relay-communication testing process were analyzed. At last, typical experimental scenes were chosen to carry out the test flight and verify the helicopter-UAV relay communication. The information exchange between the helicopter and the ground command center was realized through UAV communication relay. The performance of the communication relay met the operational requirements. The flight test verification of the second-stage cooperation of helicopters with UAVs was implemented, which can be taken as a reference for the subsequent third-stage test flights in the future.

In order to realize the system design objectives of quad-rotor UAVs, such as autonomous flying, hovering and ground-target tracking, a camera is added to the system to capture the ground information. The result of image processing is used together with sudoku segmentation method of vision to obtain the location information of the searched target. The system consists of an ultrasonic height-setting module. The motion control of UAVs is simplified from the traditional six degrees-of-freedom to four degrees-of-freedom. The location information is introduced to the PID control system, thus to obtain the control commands of the four motors, so that the motors can move in all directions. In addition, dual controllers are used. One is used to control the motion of the aircraft, and the other to control each module of the system, so as to reduce the burden of data processing and speed up the processing. Finally, the expected autonomous tracking effect is achieved. Compared with the existing methods, the motion control idea is more simplified, and the tracking of the aerial vehicle is more sensitive.

To solve the problem of “creeping” and oscillation when the tank gun controlling system operates at a low speed, a strategy of sliding-mode variable structure control based on the disturbance observer is proposed. The double closed-loop mathematical model of the current and rotary speed of the tank gun-controlling system is studied, and the sliding-mode variable structure controller is designed. A disturbance observer is designed to observe the time-varying load torque and friction torque in the control law. The simulation results show that the control strategy can not only solve the problem of “creeping” and oscillation when the tank gun controlling system operates at a low speed, but also weaken the chattering of the sliding mode caused by the modeling uncertainty.

An analogue-signal acquisition circuit with high Signal-to-Noise Ratio (SNR) is introduced based on a cooled infrared focal-plane detector. The source of the circuit noise is analyzed by using the noise analytical method, and the signal transformation path with low noise is created. Moreover, the indexes of the circuit and imaging components are analyzed by carrying out SNR test together with Noise Equivalent Temperature Difference (NETD) test. The results show that: 1) The component of SNR can reach 76 dB, and NETD can reach 17. 5 mK at environment temperature of 300 K;and 2) Less noise is introduced into the signal acquisition process, and the components can detect dim and small targets at a long distance.