## 1 Introduction  
With the rapid deployment of commercial satellite constellations [1], the number of satellites has increased dramatically. These large-scale satellite networks not only occupy valuable space resources but also pose a significant threat to the safe operation of China's space station. Issues such as the frequent orbital adjustments of satellites, collisions due to satellite malfunctions, and potential space debris [2, 3, 4, 5] have made the task of protecting the space station increasingly challenging. Although we have taken a series of measures to protect the safe operation of the space station, the existing protective measures are no longer adequate in the face of an increasingly complex space environment, especially given unexpected situations such as satellite failures. Phased array radar (PAR), also known as electric scanning radar, has a beam agility ability [6, 7, 8, 9] and can quickly complete the search and tracking of multiple targets; thus, it has been widely used. However, with the continuous increase in the number of satellites and space debris, the operational burden on phased array radar systems has significantly escalated. How to achieve the efficient and accurate tracking of multiple high-threattargets with limited radar resources has become a key issue that urgently needs to be addressed [10]. The mainstream method for resource scheduling currently is the adaptive scheduling method, which selects the optimal task execution sequence for a given scheduling interval while satisfying radar system resource constraints [11]. Many scholars have conducted in-depth research on the adaptive scheduling method. Reference [12] proposed a scheduling algorithm based on dynamic three-way decision-making, assigning different weights to different threat domains to enable more rational allocation of radar time resources during the tracking phase; reference [13] proposed a scheduling method based on particle swarm annealing algorithm, which has global search capability; However, these algorithms primarily focus on selecting scheduling moments based on the radar tasks themselves, which may lead to insufficient time utilization and the existence of idle time resources. References [14; 15; 16; 17] use a time pointer to point to the current working time of the radar. Based on a certain scheduling criterion, the most suitable task to be executed at the current time is selected for scheduling, and then the pointer slides to the end time of the task, making better use of time resources. Therefore, this algorithm is widely used in task scheduling. However, these algorithms do not utilize the time resources during the radar task's waiting period, and there is a significant deviation between the actual execution time and the expected execution time of radar tasks, leading to a high time shifting ratio (TSR) [18]. Especially for tracking tasks, the occurrence of time shifting can cause the true position of the target to gradually deviate from the predicted position, resulting in a decrease in tracking accuracy. Although pulse interleaving and time-pointer-based scheduling have both been studied in radar task scheduling, they are typically applied separately to different types of tasks. The novelty of this paper lies in integrating the two into a unified scheduling framework. The time-pointer scheduling is used to dynamically determine the execution order of tasks, while pulse interleaving is employed to fully utilize the idle periods between the transmission and reception phases of radar tasks, enabling flexible and efficient scheduling under resource-constrained conditions. In addition, a time-shifting constraint is introduced to limit the deviation of actual task execution times from their expected times, thereby ensuring more stable scheduling of high-priority tasks.