Communications and radio determination system using twogeostationary satellites. I. System and experiments

We developed two types of hybrid terminals that can provide both satellite communication and position determination services in one system. One terminal uses the single channel per carrier (SCPC) technique and the other uses the spread spectrum (SS) technique. To evaluate the performance of the two systems, we carried out experiments in Japan and in the Pacific Ocean using two geostationary satellites, ETS-V (150°E) and Inmarsat (180°E). The ranging accuracy between the mobile terminals and the base station via the satellites was found to be about 200 m using the SCPC system and about 10 m using the SS system. The measured positioning accuracy was about 1 km in the SCPC system and about 600 m in the SS system when experiments were carried out near Japan. The experimental results show that the positioning errors were mainly caused by the orbital determination errors of the two satellites. Presented here are the configurations and features of the SCPC and SS terminals, the experimental system, and the experimental results     

Energetic Particles Investigation (EPI)

The Energetic Particles Investigation (EPI) instrument operates during the pre-entry phase of the Galileo Probe. The major science objective is to study the energetic particle population in the innermost regions of the Jovian magnetosphere — within 4 radii of the cloud tops — and into the upper atmosphere. To achieve these objectives the EPI instrument will make omnidirectional measurements of four different particle species — electrons, protons, alpha-particles, and heavy ions (Z > 2). Intensity profiles with a spatial resolution of about 0.02 Jupiter radii will be recorded. Three different energy range channels are allocated to both electrons and protons to provide a rough estimate of the spectral index of the energy spectra. In addition to the omnidirectional measurements, sectored data will be obtained for certain energy range electrons, protons, and alpha-particles to determine directional anisotropies and particle pitch angle distributions. The detector assembly is a two-element telescope using totally depleted, circular silicon surfacebarrier detectors surrounded by a cylindrical tungsten shielding with a wall thickness of 4.86 g cm^-2. The telescope axis is oriented normal to the spherical surface of the Probe's rear heat shield which is needed for heat protection of the scientific payload during the Probe's entry into the Jovian atmosphere. The material thickness of the heat shield determines the lower energy threshold of the particle species investigated during the Probe's pre-entry phase. The EPI instrument is combined with the Lightning and Radio Emission Detector (LRD) such that the EPI sensor is connected to the LRD/EPI electronic box. In this way, both instruments together only have one interface of the Probe's power, command, and data unit.     

Propulsionless planar phasing of multiple satellites using deep reinforcement learning

This work creates a framework for solving highly non-linear satellite formation control problems by using model-free policy optimisation deep reinforcement learning (DRL) methods. This work considers, believed to be for the first time, DRL methods, such as advantage actor-critic method (A2C) and proximal policy optimisation (PPO), to solve the example satellite formation problem of propellantless planar phasing of multiple satellites. Three degree-of-freedom simulations, including a novel surrogate propagation model, are used to train the deep reinforcement learning agents. During training, the agents actuated their motion through cross-sectional area changes which altered the environmental accelerations acting on them. The DRL framework designed in this work successfully coordinated three spacecraft to achieve a propellantless planar phasing manoeuvre. This work has created a DRL framework that can be used to solve complex satellite formation flying problems, such as planar phasing of multiple satellites and in doing so provides key insights into achieving optimal and robust formation control using reinforcement learning.     

斜激波总压损失率极小值理论解与物理意义

以法向马赫数作为激波强度表征量,对斜激波关系式进行重新推导,得到了穿过激波总压损失率极小值的理论解。控制方程表达式为激波角对物面角的线性函数。依据斜激波总压损失率极小值解析公式,首先,绘制了针对超声速流总压损失率应用的楔形角-激波角-马赫数的斜激波效率图。其次,通过生成斜激波三维总压损失率等值线图,呈现了总压损失率在楔形角-激波角-特征马赫数空间上的分布规律。此外,利用斜激波效率图,揭示了等总压损失率条件下马赫数与激波角的对称双解现象。     

Performance analysis for DOA estimation algorithms: unification,simplification, and observations

Subspace based direction-of-arrival (DOA) estimation has motivated many performance studies, but limitations such as the assumption of an infinite amount of data and analysis of individual algorithms generally exist in these performance studies. The authors have previously proposed a unified performance analysis based on a finite amount of data and achieved a tractable expression for the mean-squared DOA estimation error for the multiple signal classification (MUSIC). Min-Norm, estimation of signal parameters using rotational invariance techniques (ESPRIT), and state-space realization algorithms. However, this expression uses the singular values and vectors of a data matrix, which are obtained by the highly nonlinear transformation of the singular value decomposition (SVD). Thus the effects of the original data parameters such as numbers of sensors and snapshots, source coherence and separations were not explicitly analyzed. The authors unify and simplify this previous result and derive a unified expression based on the original data parameters. They analytically observe the effects of these parameters on the estimation error