Due to the increasing demand for real-time observation data, remote-sensing satellites can be considered as users to access mega communication constellations, which can constitute a Low Earth Orbit (LEO) Double-Layered Satellite Network (DLSN) composed of Remote-sensing Layer Satellites (RLSs) and Communication Layer Satellites (CLSs). In this LEO DLSN, a Distributed Inter-Layer Topology Optimization (DITO) method is proposed based on time-space relationships between two layers, which can maximize task-aware observation benefits with limited onboard resources. For practical purposes, we take link switching interval constraints into account for the recapture and track of laser transceivers. Besides, we put forward a new time-slot division method based on the link switching interval for the linear modeling of the time-sequential coupling problem. With the simulation results in four constellation systems, the proposed distributed interactive method can obtain an optimal solution close to the centralized global solution with much less time. Simulation results indicate that the proposed method can support onboard optimization for future space networks.

In the future Space Information Network (SIN), remote sensing satellites (RSSs) can access communication constellations (CC) and ground stations (GSs) through inter-layer links (ILLs) and satellite-ground links (SGLs) to realize the timely transmission of large amounts of observation data. In this paper, we study the link topology of both ILLs and SGLs with different time slot durations from the perspective of each RSS based on the time-expanded graph. We propose a multi-objective mission flow optimization model to jointly achieve transmission benefits maximization, end-of-period energy maximization, and transmission wait time minimization. This model considers missions’ importance differences under limited storage, energy, and link bandwidth resources. To reduce the solving complexity, we propose a Phased Multi-Objective (PMO) algorithm composed of two Integer Linear Programming (ILP) problems and one Linear Programming (LP) problem to separate the integer programming part from the continuous part. A Multi-Objective Mixed Integer Linear Programming (MO-MILP) model is also formulated for comparison. We simulate the proposed methods in different scale SIN with practical parameters referred to the SAR satellites. The results indicate that observation data can be transmitted in near real-time with adequate bandwidth of ILLs. PMO can achieve multiple objectives and is applicable in large-scale constellation systems.

With the improvement of satellites’ maneuverability, agile earth observation satellites (AEOSs) can pitch and roll themselves to observe targets with 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 Distributed Joint Observation and Transmission Planning method for Multiple AEOSs (MA-DJOTP). This method uses a targets allocation strategy, a CS allocation model, and a targets reallocation strategy to transform the multi-AEOS problem into several single-AEOS subproblems. The Single-AEOS Joint Observation and Transmission Planning (SA-JOTP) 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 SA-JOTP 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-DJOTP algorithm in MCNs with 720 CSs and 3, 16, 36, 360 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.