# Modeling and Simulation of Sky Survey
## 2 Related Work
### Models and Algorithms  
Most of the sky survey planning models are multi-objective optimization models [30; 31] and constraint satisfaction models [32]. Heuristic algorithms are widely used in the optimization model solving for sky survey observations, such as genetic algorithms [30] and greedy algorithms [33].  
[PERSON] et al. [30] focused on the planning of the sky survey observation mode and established an intelligent planning model for the trajectory of the celestial sphere star image points. Their main objectives are: (i) to add as little attitude maneuver control as possible; (ii) to use as little energy as possible; and (iii) to achieve as many sky area scans as possible. Using a combination of orbital surface approach and orbital motion, the scanning of the celestial sphere is achieved through intelligent control of the scanning mission. While the attitude maneuver control demand on the satellite is only two times (roll angle \(\pm 30^{\circ}\) transitions), these two maneuvers divide the whole sky survey plan into three major time periods, each with an orbital approach of \(360^{\circ}\) within the duration of the time period, allowing the scanning of the entire sky area. The main constraint addressed by the planning algorithm is the energy deficit problem. To solve this problem, a multi-objective genetic algorithm is used.  
[PERSON] et al. [31] have studied, in detail, modeling the dynamics of spacecraft and cruise payloads, the constraints on sky observations, the sky area coverage model, and the payload mission planning model, respectively. The main scientific goal of conducting a 30,000 deg\({}^{2}\) survey in four years can be achieved.  
[PERSON] et al. [32] converted the survey observation scheduling problem into a constrained optimization problem and linearized the objective and constraint functions by using 0-1 decision variables and solved them using a well-established algorithm. However, the example part of the survey test data only has 30 consecutive time windows and relatively few candidate sky areas. The large-scale and long-time survey problem has not been studied.  
In addition to research on space-based survey planning, there is also research on survey planning algorithms based on ground-based telescopes. Although they differ in terms of observation environment and observation mode, there are mutual references in optimization algorithms. [PERSON] et al. [33] established a mathematical model for the Hard X-ray Modulation Telescope (HXMT) long-term mission planning problem by analyzing the optimization objectives and constraints of the problem according to the characteristics of HXMT satellites. The objective of the model is to make the observation efficiency and mission rank the highest. Based on this, a greedy algorithm mixed with a genetic algorithm is proposed as a composite solution method, and the effectiveness of the method is verified through simulation. The algorithm was tested using observation missions generated from Suzaku satellite data, and the results showed that the algorithm was effective in solving the long-term mission planning problem for the HXMT satellite, making full use of satellite resources and maximizing scientific output while meeting the constraints.