Accurate orbit prediction of lunar satellites is the foundation for providing precise positioning services to user terminals. However, due to limited ground tracking capability and simplifications in the orbital dynamics model, the prediction accuracy of lunar satellite orbits declines significantly over time when ground support is unavailable. Recently, orbit error prediction methods based on deep learning have shown the significant ability. However, exisiting deep learning models are mainly applied to Earth-orbiting satellites, and they typically extract orbital error features only in time domain or frequency domain. This results in information loss during the extraction process and limited ability to extract features from complex time signals. In this paper, we propose a novel model called DWC-Mixer for orbit correction of lunar navigation satellite, which combines frequency domain decomposition based on wavelet transform with time embedding of third body perturbation. The DWC-Mixer can effectively capture the perturbation effects not considered in the dynamical model. Extracting multi frequency and multi period variations from the orbit error sequence of the dynamic model using wavelet decomposition, ultimately achieving accurate correction of the Lunar orbit satellite trajectory prediction. First, one-dimensional convolution decomposes the lunar satellite orbit error data into several scales to obtain scale specific error variations. Next, wavelet transform and max-pooling break each scale into time and frequency domain components, extracting finer features. A time-embedding step is then applied to highlight the periodic patterns within the lunar orbit error sequence. Finally, a macro-detail fusion block recombines the refined features and feeds them into an Multi-Layer Perceptron(MLP) for training, yielding accurate orbit error predictions. We evaluate the performance on a simulated dataset. By employing the DWC-Mixer model to correct theJ2dynamic model, the ELFO orbit prediction error is reduced by 34.62% compared to other baseline network models. For PCO and LLO orbits, the errors decrease by 24.74% and 7.04%, respectively. This approach offers robust technical support for the development of lunar navigation constellations.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

The low earth orbit (LEO) remote sensing satellite has a short communication time with the earth station (ES), and a large amount of remote sensing data cannot be transmitted back to the ES in time using the LEO-ES link during the communication period. Using relay satellites can indirectly increase the amount of data transmitted back from LEO. In this paper, we combine LEO- ES link and relay satellite offloading to study the problem of maximizing the amount of data transmitted back from LEO remote sensing satellites. Most of the existing methods do not consider the effect of topology change on policy. In this paper, we consider a three-layer satellite network architecture of geostationary earth orbit (GEO), LEO remote sensing satellite , and ES. We studied the problem of maximizing the amount of LEO transmitted back data under dynamic topology between layers, and proposed a transmission strategy based on a combination of LEO-ES link and GEO offload under dynamic topology. First, in order to reduce the number of link interruptions in each time slot, a Non-Uniform Time Slot Division Method (NUTSDM) based on visible relationships between layers is proposed based on discrete-time points, which helps to accurately determine the number and identity of LEOs competing under each time slot. Second, the relationship among GEOs, LEOs, and network administrators is modeled as a Stackelberg game model, and a Two-way Bargaining Game Scheme under Dynamic Topology (TWBGS-DT) is proposed to maximize the amount of data transmitted back from space. Compared with the existing methods, the experimental results confirm the effectiveness of the proposed scheme in terms of algorithm convergence speed, terms of pricing, GEO cache space allocation, and increase the data volume of LEO transmissions back by 11.5% and 8.2 times relative to the ISL-Aided strategy and GAA-FARR strategy, respectively.