# Survey of Resource Scheduling Technologies for Ground-Based Space Target Surveillance Radar Networks Focused on Cataloging Tasks

## TL;DR

This paper reviews scheduling technologies for radar networks used in tracking space objects, focusing on efficient task allocation and optimization.

## Contribution

The paper provides a systematic review of cataloging resource scheduling methods, highlighting key subproblems and comparing algorithm performance.

## Key findings

- The cataloging task scheduling problem involves balancing multiple objectives under strict constraints.
- Multi-objective optimization algorithms show promise but face challenges in convergence and efficiency.
- Priority modeling and conflict resolution are identified as critical subproblems in scheduling.

## Abstract

Cataloging task resource scheduling is a key technology for the efficient utilization of ground-based radar networks and for supporting space situational awareness. This problem is highly challenging due to the large scale of tasks, strict time window constraints, and complex resource-task mapping relationships. It requires algorithms to effectively balance multiple conflicting optimization objectives within a huge and sparse solution space, placing extremely high demands on the convergence, diversity maintenance, and computational efficiency of the algorithms. This paper presents a systematic review of the latest research progress in cataloging resource scheduling methods. First, commonly used optimization objectives and constraint conditions in this field are outlined, and two key subproblems—priority modeling and conflict resolution—are analyzed in depth. Subsequently, following the trajectory of technological evolution, the application paradigms, performance characteristics, and limitations of mainstream algorithms are reviewed. Given the inherent multi-objective optimization nature of the problem, the advantages and challenges of multi-objective optimization algorithms are discussed. Finally, based on a unified problem context, the performance and operational boundaries of existing algorithms are compared and analyzed, and future research directions and core challenges in the field are presented.