Search Results (158)

We introduce a scalable and manufacturable approach to conformal large-scale antenna arrays, leveraging Goldberg Polyhedra configurations with hexagonal subarrays to enable cost-effective, high-performance beam steering. Planar array designs face challenges in phase control and beam deformation when steering away from the broadside, leading to increased beamwidth and degraded angular resolution. Our near-spherical Goldberg structures offer a fabrication-friendly, periodic architecture that supports industrial scalability while enabling efficient 360° digital beamforming with minimal distortion. Simulation results confirm significant reductions in sidelobe levels and improved energy concentration, providing enhanced multibeam capabilities and simplified digital beamforming (DBF) control. This approach paves the way for next-generation radar and satellite systems requiring precise directional control, minimal interference, and robust, flexible beam steering performance. Full article

Global navigation satellite system (GNSS) differential code bias (DCB) and topside ionosphere vertical electron content (VEC) can be estimated using onboard data from low-earth-orbit (LEO) satellites. These satellites provide the potential to make up for the lack of ground-based stations in the oceanic and polar regions and establish a high-precision global ionosphere model. In order to study the influences of different LEO-topside VEC processing methods on estimates, we creatively analyzed and compared the results and accuracy of the DCBs and LEO-topside VEC estimates using two topside VEC solutions—the SH-topside VEC (spherical harmonic-topside vertical electron content) and EP-topside VEC (epoch parameter-topside vertical electron content) methods. Some conclusions are drawn as follows. (1) Using GRACE-A data (400 km in 2016), the monthly stabilities (STDs) of GPS satellite DCBs and LEO receiver DCBs using the EP-topside VEC method are better than those using the SH-topside VEC method. For JASON-2 data (1350 km), the STD results of GPS DCBs using the SH-topside VEC method are slightly superior to those using the EP-topside VEC method, and LEO DCBs using the two methods have similar STD results. However, the root mean square (RMS) results for GPS DCBs using the SH-topside VEC model relative to the Center for Orbit Determination in Europe (CODE) products are slightly superior to those using the EP-topside VEC method. (2) The peak ranges of the actual GRACE-A-topside VEC results using the SH-topside VEC and EP-topside VEC methods are within 42 and 35 TECU, respectively, while the peak ranges of the JASON-2-topside VEC results are both within 6 TECU. Additionally, only the SH-topside VEC model results are displayed due to the EP-topside VEC method not modeling VEC. Due to the difference in orbital altitude, the results and distributions of the GRACE-topside VECs differ from those of the JASON-topside VECs, with the former being more consistent with the ground-based results, indicating that there may be different height structures in the LEO-topside VECs. In addition, we applied the IRI-GIM (International Reference Ionosphere model–Global Ionosphere Map) method to compare the LEO-based topside VEC results, which indicate that the accuracy of GRACE-A-topside VEC using the EP-topside VEC method is better than that using the SH-topside VEC method, whereas for JASON-2, the two methods have similar accuracy. Meanwhile, we note that the temporal and spatial resolutions of the SH-topside VEC method are higher than those of the EP-topside VEC method, and the former has a wide range of usability and predictive characteristics. The latter seems to correspond to the single-epoch VEC mean of the former to some extent. Full article

The accuracy of Precise Orbit Determination (POD) for Global Navigation Satellite Systems (GNSS) critically depends on optimal tracking station selection. This study proposed and validates a novel framework that integrates a hybrid-weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model with spherical k-means clustering, effectively resolving the challenge of balancing station data quality with uniform spatial distribution. The framework generates by first a comprehensive quality score for each station based on 40 indicators and then selects the top-scoring station from distinct geographical clusters to construct a well-distributed, high-quality network. To validate the methodology, we performed multi-GNSS POD using networks of 30, 60, and 90 stations selected by the proposed framework. The accuracy was assessed via two independent methods: orbit comparisons (Root Mean Square, RMS) against final Analysis Center (AC) orbits and Satellite Laser Ranging (SLR) validation. The results demonstrate that the optimized 60-station network (e.g., RMS of ~2.5, 5.3, 2.1, and 5.4 cm for GPS, GLONASS, Galileo, and BDS, respectively) achieves an accuracy comparable to that of a 90-station network. Moreover, a 30-station globally uniform network outperforms a 90-station network of high-quality but spatially clustered stations. This study provides an objective and quantitative solution for establishing efficient and reliable GNSS tracking networks, directly benefiting ACs and other high-precision applications. Full article

Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a method that combines an improved Phong model and higher-order spherical harmonics (HOSH) for the optical imaging computation of space objects. Utilizing HOSH to fit the light field distribution, this approach comprehensively considers direct sunlight, earthshine, reflected light from other extremely distant celestial bodies, and multiple scattering from object surfaces. Through spectral reflectance experiments, an improved Phong model is developed to calculate the optical scattering characteristics of space objects and to retrieve common material properties such as metallicity, roughness, index of refraction (IOR), and Alpha for four types of satellite surfaces. Additionally, this study designs two sampling methods: a random sampling based on the spherical Fibonacci function (RSSF) and a sequential frame sampling based on predefined trajectories (SSPT). Through numerical analysis of the geometric and radiative rendering pipeline, this method simulates multiple scenarios under both high-resolution and wide-field-of-view operational modes across a range of relative distances. Simulation results validate the effectiveness of the proposed approach, with average rendering speeds of 2.86 s per frame and 1.67 s per frame for the two methods, respectively, demonstrating the capability for real-time rapid imaging while maintaining low computational resource consumption. The data simulation process spans six distinct relative distance intervals, ensuring that multi-scale images retain substantial textural features and are accompanied by attitude labels, thereby providing robust support for algorithms aimed at space object attitude estimation, and 3D reconstruction. Full article

The design of spacecraft protection against orbital debris (OD) is generally based on experiments and models involving spherical projectiles. However, observations of collision fragments from ground-based satellite impact experiments have shown that orbital debris is non-spherical in shape. To accommodate non-spherical projectiles in spacecraft protection measures, a relationship between spherical projectiles and their threat-equivalent non-spherical counterparts was established. Cylindrical projectiles featuring adjustable Length-to-Diameter (L/D) ratios were employed to simulate the projectile shape effect on the bumper performance under hypervelocity impact. The L/D ratio spanned a range from L/D = 1/3, representing a “flake” shape, through L/D = 1 for a “nugget” configuration and extended up to L/D = 5/3, representing a “straight rod” configuration. The numerical analysis utilized the smoothed-particle hydrodynamics technique, demonstrating that projectile geometry significantly influenced the threat posed by projectile fragments to the objects behind the bumper. The established projectile threat relationship can be applied to assess the ability of the existing OD bumpers to withstand non-spherical projectiles by representing them with an equivalent sphere. Utilizing this approach can contribute to decreasing uncertainty and enhancing the protection of spacecraft when encountering irregularly shaped OD particles. Full article

Metal additive manufacturing represents the most promising application for Three-dimensional printing systems. Gas atomization is an important method for the preparation of metal powders. In this work, aluminum powders were prepared via vacuum induction atomization. Morphology and microstructure were investigated. Results demonstrated that optimal atomization occurred at 3 MPa with a 2.5 mm delivery tube diameter, yielding powders with an average particle size of 63.8 μm and a sphericity of more than 90%. Variations in droplet size caused differential cooling rates, enabling rapidly solidified small droplets to adhere to incompletely cooled large droplets and form satellite spheres. Quantitative analysis confirmed a positive correlation between metal powder particle size and surface satellite spheres. Large droplets exhibited long cooling times, resulting in more pronounced dendritic microstructures. This study provides theoretical insights for the preparation of metal powders. Full article

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

Agile Earth Observation Satellites (AEOSs), with their maneuverability, can flexibly observe point, line and region targets. However, existing research typically requires distinct algorithms for each target type, lacking a unified modeling and solution framework, which hinders the ability to meet the demands of rapid and coordinated observation of multiple target types in complex scenarios. To address these issues, this paper proposes a unified scheduling model for agile Earth observation satellites based on the Degenerate Quadtree Grid (DQG) and Proximal Policy Optimization (PPO), termed AEOSSP-USM. Firstly, the DQG is first employed to enable unified management and integrated modeling of point, line, and area targets; Secondly, traditional time window calculations based on longitude and latitude are replaced with grid code-based computations using DQG; Finally, the PPO algorithm, a deep reinforcement learning method, is introduced to formulate AEOSSP-USM as a Markov Decision Process (MDP), enabling efficient problem solving. Experimental results demonstrate that the proposed method effectively realizes unified scheduling of heterogeneous targets, improving imaging quality about 3 times, reducing energy consumption by 10%, decreasing memory usage more than 90%, and enhancing computational efficiency by 35 times compared to conventional longitude-latitude strip algorithm. Full article

This study aims to enhance Low Earth Orbit (LEO) satellite orbit prediction accuracy. We propose the Precise Orbit Determination with Optimized Perturbations (PODOP) method, considering Earth’s non-spherical gravity, atmospheric drag, etc., and a Long Short-Term Memory (LSTM)-based approach for orbital element time series. Validation shows that PODOP’s 10-day median error is 8.1 km (19% larger than Simplified General Perturbations (SGP4)’s 10.1 km) and LSTM’s 10-day median error is 5.3 km, outperforming SGP4 (48.5 km) and PODOP and improving constellation management and collision prevention. Full article

Accurate estimation of the regional ionospheric model (RIM) is essential for Total electron content and high-precision applications of the Global Navigation Satellite System (GNSS). Utilizing dual-frequency observations from over 250 Crustal Movement Observation Network of China (CMONOC) monitoring stations, which are equipped with both GPS and GLONASS receivers, this study investigates the Vertical Total Electron Content (VTEC) estimation models over the China region and evaluates the estimation accuracy under both GPS-only and GPS+GLONASS configurations. Results indicate that, over the Chinese region, the spherical harmonic reginal ionospheric model (G_SH RIM) and polynomial function reginal ionospheric model (G_Poly RIM) based on single GPS observations demonstrate comparable accuracy with highly consistent spatiotemporal distribution characteristics, showing grid mean deviations of 1.60 TECu and 1.62 TECu, respectively. The combined GPS+GLONASS observation-based RIMs (GR_SH RIM and GR_Poly RIM) significantly improve the TEC modeling accuracy in the Chinese peripheral regions, though the overall average accuracy decreases compared to single-GPS models. Specifically, GR_SH RIM and GR_Poly RIM exhibit mean deviations of 2.15 TECu and 2.32 TECu, respectively. A preliminary analysis reveals that the reduced accuracy is primarily due to the systematic errors introduced by imprecise differential code biases (DCBs) of GLONASS satellites. These findings can provide valuable references for multi-GNSS regional ionospheric estimation. Full article

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

In this manuscript, we study the coverage of convex spherical domains by spherical circles. This question can be applied to the location of satellites, weather balloons, radio towers, etc. We present an upper bound on the number of spherical circles of radius r needed to cover a spherical convex domain K, in terms of the respective area and perimeter. Then, we calculate the asymptotic density of such cover, when the radius approaches zero. Full article

As a major agricultural center in China, Henan Province is highly dependent on groundwater resources for its socioeconomic development. However, under the triple pressure of intensive agricultural irrigation, surging industrial water demand, and accelerating urbanization, the sustainable use of groundwater resources has become a key issue for regional development. This paper utilizes GRACE satellite data and the Global Land Data Assimilation System (GLDAS) assimilation model from 2003 to 2023 to invert alterations in terrestrial water storage (TWS) and groundwater storage (GWS) in Henan Province. We examine the factors influencing these changes and compare the spherical harmonic coefficient (SH) data with Mascon data, integrating precipitation and soil moisture data. Using the GRACE Mascon data as a reference, GWS in Henan Province exhibited a stable trend from January 2003 to October 2010, with a rate of −0.060 cm/month. From October 2010 to June 2020, GWS demonstrated a declining trend, with a rate of −0.121 cm/month. Conversely, from June 2020 to December 2023, GWS revealed a significant upward trend, with a rate of 0.255 cm/month. The TWS and GWS of the inverse performances of the Centre for Space Research (CSR) SH data and the CRS Mascon data exhibited a similar trend, albeit with differing values. Additionally, the precipitation data, soil moisture, and GLDAS data demonstrated significant seasonal variations, with a lag of approximately two months between changes in precipitation and GWS. Declining GWS could be related to climatic and anthropogenic factors. The changes in groundwater in Henan Province studied in this paper can provide a reference for the sustainable utilization of groundwater resources in the region. Full article

A long-wavelength geoidal geometry reflects mainly lateral density variations in the Earth’s mantle, with the most pronounced features of the Indian Ocean Geoid Low and the West Pacific and North Atlantic Geoid Highs. Despite this spatial pattern being clearly manifested in the global geoidal geometry determined from gravity-dedicated satellite missions, the gravitational signature of the deep mantle could be refined by modelling and subsequently removing the gravitational contribution of lithospheric geometry and density structure. Nonetheless, the expected large uncertainties in available lithospheric density models (CRUST1.0, LITHO1.0) limit, to some extent, the possibility of realistically reproducing the gravitational signature of the deep mantle. To address this issue, we inspect an alternative approach. Realizing that the gravity geopotential field (i.e., gravity potential) is smoother than its gradient (i.e., gravity), we apply the integral operator to geopotential and then investigate the spatial pattern of this functional (i.e., radially integrated geopotential). Results show that this mathematical operation enhances a long-wavelength signature of the deep mantle by filtering out the gravitational contribution of the lithosphere. This finding is explained by the fact that in the definition of this functional, spherical harmonics of geopotential are scaled by the factor 1/n (where n is the degree of spherical harmonics), thus lessening the contribution of higher-degree spherical harmonics in the radially integrated geopotential. We also demonstrate that further enhancement of the mantle signature in this functional could be achieved based on modelling and subsequent removal of the gravitational contribution of lithospheric geometry and density structure. Full article

Orbit determination for non-cooperative low Earth orbit (LEO) objects undergoing continuous low-thrust maneuvers remains a significant challenge, particularly for large satellite constellations like Starlink. This paper presents a method that integrates the unscented transformation into a batch filtering framework with an optimized rho-minimum sigma points sampling strategy. The proposed approach uses a reduced dynamics model that considers Earth’s non-spherical gravity and models the combined effects of low-thrust and atmospheric drag as an equivalent along-track acceleration. Numerical simulations under different measurement noise levels, initial state uncertainties, and across multiple satellites confirm the method’s reliable convergence and favorable accuracy, even in the absence of prior knowledge of the along-track acceleration. The method consistently converges within 10 iterations and achieves 24 h position predictions with root mean square errors of less than 3 km under realistic noise conditions. Additional validation using a higher-fidelity model that explicitly accounts for atmospheric drag demonstrates improved accuracy and robustness. The proposed method can provide accurate orbit knowledge for space situational awareness associated with continuously maneuvering Starlink satellites. Full article