测量带有常值偏差时卫星自主定轨系统可观性分析

考虑测量带有常值偏差时卫星自主定轨系统的状态观测和常值偏差估计问题,将观测常值偏差扩增为状态后进行卡尔曼滤波是解决这一问题的一个途径。为使卡尔曼滤波稳定,就必须分析系统的随机可观测性。文章利用线性时变系统的可观性理论对测量带有常值偏差时卫星自主定轨系统的可观性进行了分析,数值仿真结果显示了所得理论的正确性。     

分布式SAR超分辨成像的卫星编队构形优化设计方法

编队构形设计是分布式卫星系统总体设计的关键问题,提出了一种编队构形优化设计方法。针对主星带辅星群体制分布式合成孔径雷达(SAR)超分辨率任务,从优化系统效能的角度出发,将满足超分辨性能的时间比率和分辨率改善因子作为编队构形优化设计的目标函数,以基线矢量为中间变量,建立了目标函数与编队卫星轨道根数的关系,并采用遗传算法求解。对不同卫星数目的编队系统进行了优化设计仿真,并与干涉车轮和钟摆编队构形方案进行了分析比较,数值结果验证了该优化设计方法的有效性和正确性。该设计方法还可改变优化目标函数,以适应用户的任务要求。     

非合作目标自主交会对接的椭圆蔓叶线势函数制导

针对非合作式航天器自主交会对接任务的安全性要求,提出了一种基于椭圆蔓叶线的人工势函数制导方法。首先根据视线坐标系建立了相对动力学方程与状态方程。进而应用人工势函数制导方法解决了非合作目标航天器自主交会对接与静态障碍物躲避问题,并且把势函数方法与椭圆蔓叶线函数相结合,解决了追踪航天器在接近目标航天器时运行在安全走廊中的安全性要求。应用Lyapunov稳定性理论证明了在所提出的制导方法控制下系统的稳定性。最后,用精确的数学模型进行了计算机数值仿真,验证了所提出的制导控制方法的正确性和有效性。
     

Interaction of oblique wave beam with ionospheric layer F2

Interaction of a powerful obliquely incident wave beam of decameter radio waves with the ionospheric F2 layer is analyzed. Much like the linear case, propagation through the natural anti-waveguide layer F2 splits the initial beam. Some part of its energy leaks through the ionospheric layer, the other part goes back along a downward trajectory. However, nonlinearity leads to further stratification of the ionospheric layer. A new feature, in comparison with the linear case, is appearing a narrow waveguide beneath the F2 layer maximum which traps a small part of the beam energy. We study the relationship between these parts of the wave field in a simplified model of parabolic F2 layer, with nonlinearity caused by thermal plasma expulsion from the high field intensity region. Analytical results are supplemented with numerical estimates of the effects.     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.     

Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

Precise output loads control of load-diffusion components with topology optimization

The purpose of this paper is to present a novel topology optimization approach to control precisely the output loads under static loads and harmonic excitations. We introduce the Artificial Bar Element (ABE) at the designated output positions, where the output loads are equivalently measured and constrained with the nodal displacements of ABE. Optimization model is then formulated considering the output load constraints as well as the minimization of strain energy and dynamic displacement responses respectively under the static and dynamic conditions. The influences of the ABEs stiffness, different material usages of the design domain, widths of the output loads constraint intervals and variation ratios of output loads are discussed in detail. The proposed method is verified with several numerical examples with clear and reasonable load transfer paths.     

The lower ionosphere response to its disturbances by powerful radio waves

The paper presents data from some campaigns at Sura heating facility in 2011–1016. The experiments on probing of the artificial disturbed region of the lower ionosphere were carried out at two observation sites. One of them was located near Vasil’sursk 1 km from Sura facility (56.1°N; 46.1°E) and the other site was located at the Observatory (55.85°N; 48.8°E) of Kazan State University, 170 km to the East. Investigation of the features of the disturbed region of the lower ionosphere based on its diagnostics by the methods of the vertical sounding and oblique backscattering is the main goal of this paper. Ionosphere disturbance was fulfilled by the effect of the powerful radio wave of the ordinary or extraordinary polarization emitted by transmitters of the Sura facility with effective radiated power ERP = 50–120 MW at the frequency of 4.3, 4.7 and 5.6 MHz. Pumping waves were emitted with period from 30 s to 15 min. The disturbed region of the ionosphere in Vasil’sursk was probed by the vertical sounding technique using the partial reflexion radar at the frequency of 2.95 and 4.7 MHz. For the oblique sounding of the disturbed region the modified ionosonde Cyclon-M, operating at ten frequencies from 2.01 to 6.51 MHz was used at the Observatory site. On many heating sessions simultaneous variations of the probing partial reflection signals in Vasil’sursk and backscattered signals in Observatory were observed at the height at 40–100 km below the reflection height of the pumping wave. These observations were correlated with the pumping periods of the Sura facility. Possible mechanisms of the appearance of the disturbance in the lower ionosphere and its effect on the probing radio waves are discussed.     

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.     

Deep reinforcement learning for six degree-of-freedom planetary landing

This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system capable of estimating the lander’s state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander’s estimated state directly to a commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system’s performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.