# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 1 Introduction  
Low-cost Distributed Satellite Systems (DSS) will potentially play a significant role in the design of future space missions. These systems will collaborate to accomplish difficult mission goals. Real-time multi-spacecraft coordination, data processing, and prioritisation will not only optimise mission science return by establishing observational parameters of interest or success, but it will also facilitate outer solar system missions and missions in extreme environments (e.g., Io, Venus, subsurface Europa) where communication with ground operations and ground-based analysis times are limited. This will allow for a greater return on investment in mission science. This capability will enable previously unthinkable classes of missions by providing levels of autonomy that are unparalleled in the sector [1; 2; 3]. It will be necessary to make significant advances in the capabilities of the architectures that are used to implement these envisioned space missions in order to be able to carry them out. DSSs are comprised of a large number of spacecraft that cooperate with one another to accomplish a specific mission objective [4]. In some circumstances, DSSs combine to generate a sensory system that would be impossible to create on a monolithic platform [5; 6]. In other configurations, they use distributed measurements to extract data on the spatial and temporal consequences of phenomena that are far larger than what a single spacecraft can observe due to limited swath width and duty cycle [7; 8]. There has been a great deal of interest in addressing the technology required to enable new applications, particularly in cases where DSS-dependent missions are becoming increasingly important. On-board processing, inter-satellite networks, and autonomous decision making are the primary focuses of this article. These three technologies are interdependent and cannot exist without one another. For example, the processors that are implemented on board must have sufficient processing capability in order to process the data that are necessary for drawing conclusions as well as any computation that may be involved in the process of making decisions that are time-sensitive or computationally intensive. In addition, owing to the inter-satellite networks, the DSS spacecraft are able to communicate with one another and coordinate their activities without the need for immediate ground control. Because the ground link has a restricted bandwidth and latency, this feature is critical, especially for activities that take place in outer space [9; 10]. A mission's risk is reduced when it is spread among several launches, ensuring that the entire system is not destroyed in the event of a launch failure. Additionally, it provides the option to gradually build the system in orbit, allowing for the construction of various modules at various stages. The modular architecture theory serves as the foundation for DSS architecture. A study by the Research and Development (RAND) Project air force shows that [11]:  
1. Distributed constellations may weigh less and cost less to launch. 2. Distributed satellites may perform better during deployment. 3. Distributed satellite constellations may be able to fail more gracefully. 4. Distributed satellite constellations may be more survivable in a cyber attack. The primary goal of a DSS is to deliver a more responsive and resilient solution to meet the expanding demands of the scientific community and also the defence sector by aiding in the measurement and prediction of Earth Observation (EO) missions [12] and Space Based Space Surveillance (SBSS) missions [13; 14; 15; 16; 17; 18] in the hope of enhancing space sustainability. Distributed satellite architectures can also be classified as formation-flying missions. Examples include PRISMA [19; 20], GRACE [21], and TerraSAR-X-TanDEM-X [22]. DSS are categorised based on the type of mission and function they perform.