With the improvement of satellites' maneuverability, agile earth observation satellites (AEOSs) can pitch and roll themselves to observe targets with a longer visible time window (VTW), which enables more targets to be observed while bringing greater uncertainties of mission planning and more conflicts of resources. Meanwhile, mega constellation networks (MCNs) provide powerful tools to transmit massive observation data. In MCNs, AEOSs can observe targets agilely and access communication satellites (CSs) by inter-satellite links (ISLs) to offload data. Based on this architecture, we propose a Stratified Programming method of Joint Observation and Transmission planning for Multiple AEOSs (MA-SPJOT). This method comprises centralized mission allocation methods and a distributed Single-AEOS Joint Observation and Transmission (SA-JOT) model. The centralized allocation methods allocate targets and CSs to specific AEOSs to transform the multi-AEOS problem into several single-AEOS subproblems. The SA-JOT model is formulated as a Mixed Integer Quadratic Constraint Programming (MIQCP) problem based on a mission-based time slot division method, which can help simplify the observation time determination and ISL handover modeling. The proposed model can realize both the benefit maximization and the transmission delay minimization based on practical constraints of mission transition time, laser ISLs' characteristics, and limited onboard resources. We verify the effectiveness of the proposed MA-SPJOT algorithm in MCNs with 720 CSs and different numbers of AEOSs. The results show that the proposed algorithm can obtain a solution very close to the global optimum of the centralized method and is applicable in MCNs with hundreds of satellites.

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.