A Unified Approach For Optical Survey Strategy Design of Resident Space Objects Akhter Mahmud Nafi * , Arun Bernard * , and Kohei Fujimoto † Utah State University, Logan, UT, 84322-4130, USA Orbital debris poses an increasing risk to manned space missions and operational satellites. As larger networks of electro-optical (EO) sensors are tasked for space surveillance in order to improve spatiotemporal knowledge of the ever-growing resident space object (RSO) population, it is imperative that survey designs be autonomously generated such that they optimally balance the visibility, coverage, and estimation accuracy of RSOs as per the survey objective. To this end, we propose a framework to quantitatively evaluate designs of EO RSO surveys. Where appropriate, we inherit existing language and conventions regarding EO sensor and survey design. These functions exhibit good linearity for the domain of angle and angle-rate measurements that are usually captured in a single image, so a rapid linear evaluation is locally feasible. A simulation comparing the visibility and coverage of two survey designs in the near geostationary orbit regime aligns with observer expectations. I. Introduction Orbital debris poses an increasing risk to manned space missions and operational satellites. 1 The majority of debris between 1 cm to 10 cm in diameter – i.e., those large enough to cause catastrophic damage upon collision – is currently not being tracked and maintained in a catalog. Thus, better space situational awareness (SSA) is required to avoid collisions between orbiting satellites and debris, provide safe reentries, detect on-orbit explosions, and assist missions at launch. 2 Radar and the electro-optical (EO) sensors are the two primary methods for detecting, tracking, identifying, and characterizing RSOs, where EO is the workhorse of surveillance in medium Earth orbit (MEO) and above. In the design of optical surveys for resident space objects (RSOs), the naïve goal would be to strategically point the telescope so as to detect the maximum amount of the target RSO population with maximum accuracy in minimum time. In reality, the balance between demands for visibility (i.e., how many photons we can capture of each RSO), coverage (i.e., how much of the target RSO population is observed), and accuracy (i.e., how much state information may be extracted from RSO measurements) would largely depend on the goal of the survey. For instance, if a particular survey aims to find new high area-to-mass ratio (HAMR) objects, visibility should be prioritized, as the brightness of HAMR objects have high temporal variance. On the other hand, for a survey aimed to maintain and expand the geostationary (GEO) belt catalog, the lack of dynamics for these objects relative to a ground-based observer would suggest that observability be prioritized. All the while, coverage is to be maximized to detect new objects in a timely fashion. As larger networks of optical sensors are tasked for space surveillance in order to improve spatiotempo- ral knowledge of the ever-growing RSO population, it is imperative that survey designs be autonomously generated such that they optimally balance the three criteria above as per the survey objective. The existing literature, albeit mature, is not conducive to such goals. Subsequently, current SSA operations tend to rely on observer experience and empirical rules. Papers detailing methods on how to individually maximize vis- ibility, 3–6 coverage, 7–9 or accuracy 10, 11 exist – refer to 12 for an overview – but they do not quantitatively study the trade between observation geometry and cadence. A more rigorous approach bridging observability and visibility was studied by Hussein, et al., 13, 14 where sensor allocation was optimized so as to maximize information gain in the 0-2 object joint detection and tracking problem. Object visibility was implemented as the probability of detection in a finite set statistics (FISST) filter framework. On the other hand, in the * Graduate Student Research Assistant, Department of Mechanical and Aerospace Engineering, 4130 Old Main Hill † Assistant Professor, Department of Mechanical and Aerospace Engineering, 4130 Old Main Hill, AIAA member 1 of 14 American Institute of Aeronautics and Astronautics