平流层飞艇的组合导航研究

针对平流层飞艇的飞行特点,提出SINS/GPS/陆标组合导航方法,给出SINS/陆标组合导航的观测模型,并将状态与偏差分离的估计算法与联邦滤波相结合,应用于SINS/GPS/陆标组合导航系统.数学仿真结果表明:新组合导航系统相对于SINS/GPS组合导航系统可以有效改善平台误差角的估计精度,同时,提出的简化状态与偏差分离联邦滤波算法能在保持滤波精度基本不变的前提下减少计算量.     

煤体变形局部化的梯度塑性理论研究

摘 要 本文采用白光数字散斑相关方法对煤在单轴压缩下变形破坏进行研究，定量的测出了煤的变形局部化带宽度。并通过应变梯度塑性理论，在屈服函数中引入应变梯度二阶项。采用Drucker－Prager准则解析得出了煤试件的变形局部化带宽度公式并根据试验确定煤的材料内部长度。     

基于循环前缀的OFDM符号同步改进算法研究

OFDM系统的接收端对同步性能的要求很高,同步不好对整个系统性能的影响非常大。符号定时同步是同步过程中首先进行的,文中提出一种基于循环前缀的符号同步改进算法,相比于最大似然估计算法,改进算法运算量小、实现简单。两种算法的仿真结果表明,改进算法对循环前缀长度的要求要低得多,并且估计性能得到了一定的提高。     

偏置动量卫星偏航姿态估计与控制研究

为改善偏置动量卫星无偏航姿态敏感器时用滚动角间接控制偏航姿态的精度,基于降维观测器设计提出了一种采用偏航角估计值直接控制偏航姿态方法。在分析收敛性的基础上,给出了降维观测器的设计,以及直接控制法的系统模型。理论分析和仿真结果表明,该法能大幅提高偏置动量卫星的姿态精度,且易于软件实现,不增加系统硬件配置。     

中远程面（空）对空导弹末制导段的机动目标“标架”模型及自适应估计算法

根据中远程导弹制导系统大都采用脉冲多卡勒雷达导引头的趋势和中远程防空导弹末制导实用对目标机动运动状态的需求，基于目标螺旋机动和蛇行机动的切加速度和法向加速度在目标轨迹活动框架上缓变的特点，本文提出了一种建立在目标轨迹活动框架上的运动状态方程和自适应Kalman估计算法，该方法克服了在直角坐标系和球坐标系上估计目标快变加速度机动延迟大的缺陷，仿真实验证明该算法有很好的跟踪稳定性和收敛性，为先进末制导律的实现创造了条件。     

基于灰度预测的无刷直流电机控制

针对传统PID控制器抗干扰能力差，参数调节繁琐等问题，提出了灰度预测控制器。对灰度预测算法进行了详细的理论分析和数学建模，利用最速梯度法对控制器参数进行调整，设计了参数自适应调整的控制器；对飞轮使用的无刷直流电机进行了较为准确的模型建立，并在此基础上设计了基于灰度预测控制器的电机控制仿真模型。通过仿真分析了两种控制器在正常加速和受到干扰两种情况下的不同表现，验证了灰度预测控制器在调节效果和稳定性等方面的优越性。     

飞机结构装配间隙超差分析与对策

飞机结构装配过程中,间隙、阶差超差类问题比较普遍,如机身部段之间、舵面与结构之间、相邻整流罩壁板之间等.通过对典型的间隙、阶差超差案例进行分析,总结造成间隙、阶差超差的主要原因,并提出解决间隙及阶差超差类问题的系统思路及指导方法.     

数字电视大范围载波频偏快速捕获算法

根据中国数字电视地面传输标准帧结构特点,提出了一种大范围频率偏差捕获方法.该算法利用PN(Pseudo Noise)序列移位相加后仍为PN序列这一特征,通过帧头PN序列与本地PN序列的差分互相关得到待求的频偏值,然后利用BLUE(Best Linear Unbiased Estimator)方法,在不损失频偏精度的前提下扩大频偏估计范围.该算法能在帧同步前快速捕获大范围频偏,为可靠实现帧同步以及频偏跟踪过程提供了必要条件.仿真结果表明,相对于传统的差分互相关算法,该算法能在一个帧头时间内快速锁定频偏,频偏估计范围扩大了H倍且频偏精度不变.     

Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators(CIs) are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator(HI)through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system's reliability.     

A comparative analysis of SLR,MLR, ANN,XGBoost and CNN for crop height estimation of sunflower using Sentinel-1 and Sentinel-2

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (?VH, ?VV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ?VH, ?VV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies.