Pro- and Retrospective highly accurate and consistent Earth Orientation parameters for Geodetic Research within the Earth System Sciences (PROGRESS)

准确了解地球在空间的方位和自转对于近地和深空通信、全球定位和导航以及监测全球变化现象等各种科学和社会应用至关重要。地球定向参数（EOP）描述了从太空（天体中间极（CIP）偏移Δ XCIP和Δ YCIP）和从地球表面（极移（PM）或地球中间极（TIP）偏移xpole和ypole）看到的地球旋转轴的位置。此外，EOP描述了地球绕其旋转轴的旋转（角速度，Δ UT1）。除了偏移量之外，EOP还包括以CIP和PM速率以及LOD（日长）的形式的地球定向和旋转的时间变化。虽然CIP偏移量可以非常精确地建模，但地球自转参数（ERPs），即PM，Δ UT1及其速率，可以通过四种空间大地测量技术获得：卫星和月球激光测距（SLR，LLR），多普勒轨道测量和卫星集成无线电定位（DORIS），全球导航卫星系统（GNSS）和甚长基线干涉测量（VLBI）。目前的EOP组合方法和预测缺乏一致性，不再满足最先进的精度要求。该项目旨在开发最终和快速ERP的最佳组合策略，以及基于有效角动量（EAM）数据的预测。因此，重点将放在所有未知参数和均匀背景模型的一致数学建模上。此外，该项目的重点是在正规方程（NEQ）水平上通过适当的滤波（基于卡尔曼滤波器或信息滤波器）扩展的精细组合的开发。对于预测，将进一步开发基于EAM数据的方法，研究使用人工智能（AI）或人工神经网络（ANN）方法的潜力。

The accurate knowledge of the Earth’s orientation and rotation in space is essential for a broad variety of scientific and societal applications such as near Earth and deep space communication, global positioning and navigation as well as monitoring of global change phenomena. The Earth Orientation Parameters (EOPs) describe the position of the Earth’s rotation axis as seen from space (Celestial Intermediate Pole (CIP) offsets delta XCIP and delta YCIP) and as seen from the Earth’s surface (Polar Motion (PM) or Terrestrial Intermediate Pole (TIP) offsets xpole and ypole). Moreover, the EOPs describe the rotation of the Earth around its rotation axis (angular velocity, delta UT1). Beside the offsets, the EOPs also comprise the temporal variation of the Earth’s orientation and rotation in form of CIP and PM rates and LOD (Length Of Day). While the CIP offsets can be modelled very accurately, the Earth Rotation Parameters (ERPs), namely PM, delta UT1 and their rates, are accessible via the four space geodetic techniques Satellite and Lunar Laser Ranging (SLR, LLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), Global Navigation Satellite Systems (GNSS), and Very Long Baseline Interferometry (VLBI). Current EOP combination approaches and predictions lack consistency and do not longer fulfil state-of-the-art accuracy requirements. This project aims at the development of an optimal combination strategy of final and rapid ERPs together with a prediction based on Effective Angular Momentum (EAM) data. Thereby, the focus will be put on a consistent mathematical modelling of all unknown parameters and homogeneous background models. Moreover, the focus of the project is on the development of a refined combination at normal equation (NEQ) level extended by an appropriate filtering (Kalman- or information-filter based). For the prediction, approaches based on EAM data will be further developed, investigating the potential of the use of Artificial Intelligence (AI) or Artificial Neural Network (ANN) methods.