High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation. Polar motion is affected by a variety of excitation factors, and nonlinear prediction methods are more suitable for polar motion prediction. In order to explore the effect of deep learning in polar motion prediction. This paper proposes a combined model based on empirical wavelet transform (EWT), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). By training and forecasting EOP 20C04 data, the effectiveness of the algorithm is verified, and the performance of two forecasting strategies in deep learning for polar motion prediction is explored. The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days, while direct multi-step prediction is more suitable for medium and long-term forecasts. In the 365 days forecast, the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas, respectively, which is 23.5% and 16.2% higher than the accuracy of Bulletin A. The results show that the algorithm has a good effect in medium and long term polar motion prediction.

The moon, as the Earth’s sole natural satellite, holds significant importance in human space exploration and serves as a fundamental platform for further deep space endeavors. Cislunar space represents an extensive domain for expanding human habitation beyond terrestrial land and oceans. The exploration, construction, and development of cislunar space have generated increasing demands for cislunar navigation capabilities. Currently, GNSS navigation technology has been well-established for Earth and near-Earth regions and is now being extended to encompass the cislunar realm. Simultaneously, advancements in small satellite technology offer new possibilities for efficient and rapid deployment of small satellite navigation constellations. In response to the requirements of cislunar navigation, this paper proposes the establishment of a small-space based navigation system utilizing BDS (BeiDou Navigation Satellite System) time-transfer technology on a small satellite platform within earth-moon libration point orbits. Specifically, multiple types of orbits including DRO (Distant Retrograde Orbit) and NRHO (Near Rectilinear Halo Orbit) are employed to form a navigational constellation under the framework of a small satellite platform operating in earth-moon libration point orbits; BDS timing is utilized to compensate for limited payload capacity on small satellites as well as limitations in star clock accuracy. This paper presents the design architecture analysis of such a cislunar space small-satellite navigation system employing BDS time-transfer technology while systematically studying various multi-orbital characteristics adopted by this system along with its corresponding navigational features. Furthermore, observability analysis is conducted on these navigational constellations while evaluating their performance through relevant accuracy indicators across several constellation design scenarios. The feasibility and preliminary performance of the cislunar space small satellite navigation system using BDS as time-transfer are verified in this paper, providing a valuable reference for BDS and GNSS to offer navigation services in cislunar space. A novel idea and approach for lunar space and southern hemisphere moon navigation are proposed.

Given that the observations from current space geodetic techniques do not carry all the necessary datum information to realize a Terrestrial Reference System (TRS), and each of the four space geodetic techniques has limits, for instance: Very Long Baseline Interferometry (VLBI) ignores the center of mass and satellite techniques lack the TRS orientation, additional constraints have to be added to the observations. This paper reviews several commonly used constraints, including inner constraints, internal constraints, kinematic constraints, and minimum constraints. Moreover, according to their observation equations and normal equations, the similarities and differences between them are summarized. Finally, we discuss in detail the influence of internal constraints on the scale of VLBI long-term solutions. The results show that there is a strong correlation between the scale parameter and the translation parameter introduced by the combination model at the Institut National de l’Information Géographique et Forestière (IGN), and internal constraints force these two groups of parameters to meet certain conditions, which will lead to the coupling of scale and translation parameters and disturbing the scale information in VLBI observations. The minimum or kinematic constraints are therefore the optimum choices for TRF.

Low Earth orbit (LEO) constellations are increasingly deployed to provide integrated services such as communication, navigation augmentation, and remote sensing. However, traditional constellation designs—such as Walker, streets‐of‐coverage (SoC), and Flower constellations—rely on distinct parameter sets, making unified optimization of hybrid configurations challenging. Existing unified approaches like the continuous coefficient ( C ² ) method suffer from high‐dimensional parameter spaces, leading to suboptimal solutions and convergence issues. To address this, we propose an improved continuous coefficient ( i − C ² ) method that reduces the number of real‐valued parameters while maintaining the ability to represent both symmetric and asymmetric constellation types. Using evolutionary algorithms, we apply the i − C ² method to design several LEO constellations, including single‐ and dual‐coverage SoC configurations, a navigation‐augmentation hybrid constellation, and an integrated positioning, navigation, timing, remote sensing, and communication (PNTRC) system. Results demonstrate that the i − C ² method successfully reproduces the Iridium NEXT constellation and identifies “inclination family” structures. Compared to combined orthogonal circular and Walker constellations, the i − C ² method improves the uniformity of the average number of visible satellites by 20% and 77.92% for 100‐ and 150‐satellite configurations, respectively. This study highlights the i − C ² method’s capability to enable efficient, flexible, and high‐performance LEO constellation design within a unified optimization framework, offering significant potential for more integrated and cost‐effective satellite systems.

Imaging based on signals reflected from global navigation satellite systems (GNSS) represents a novel approach for surface observation. Owing to the global coverage of navigation satellite constellations and the short revisit periods of satellite sub-points, GNSS reflectometry imaging enables high-temporal-resolution change detection in sensitive areas. However, stationary dominant reflectors (SDRs) in the imaging area suppress the signal amplitudes of nearby weak reflection targets, thereby increasing the difficulty of their detection. Conventional coherent change detection and differential interferometry methods demand high phase accuracy and require multiple imaging operations within the imaging domain, thereby reducing detection efficiency. To address these issues, this study proposes a method for directly suppressing SDRs directly in the range-compressed domain. This method leverages the extensive cancellation algorithm batches (ECA-B) framework and utilizes a single satellite revisit period to construct a scattering characteristic model dominated by SDRs, which serves as the reference signal for ECA-B cancellation. To ensure model stability, two-dimensional range-compressed results from multiple prior revisit periods were accumulated to construct a weighted scattering characteristic model, incorporating noise mean and variance. Simulations and real-world experiments demonstrated that the proposed method significantly attenuates signals from strong reflectors in the range-compressed domain, thereby enhancing the detectability of weak change targets.