###### Abstract
In addition, the limited availability of receiver channel resources, coupled with the necessity of frequent antenna adjustments for satellite tracking, further complicates the LEO satellite navigation and positioning system. [PERSON] et al. [14] and [PERSON] et al. [15; 16] employed carrier phase tracking technology to precisely locate non-cooperative signals and successfully achieved meter-level positioning accuracy with Starlink constellation signals through utilizing the unmodulated carrier embedded in the signal header. However, the rapid motion of LEO satellites relative to ground receivers is susceptible to inducing Doppler frequency shifts, thereby complicating signal reception and processing. To address this challenge, carrier Doppler measurements are commonly employed for positioning purposes [17]. For the optimal selection of satellites in LEO satellite constellations, [PERSON] et al. [18] formulated a minimization problem targeting Doppler Geometric Dilution of Precision (D-GDOP). Notably, DGDOP is influenced by the velocities and accelerations of the visible satellites. When the number of phased array antennas is limited, it becomes essential to select satellite combinations that exhibit superior geometric configurations and extended visibility durations in real time. Optimizing the satellite selection algorithm can decrease the number of satellite switches, prolong continuous observability, and enhance the continuity and stability of satellite signal reception, as well as improve the precision of satellite navigation and positioning. The current research on satellite selection algorithms is primarily categorized into traditional geometric optimization algorithms and intelligent optimization-based satellite selection algorithms. The traditional geometric optimization algorithm focuses on minimizing the Geometric Dilution of Precision (GDOP) as its primary objective and identifies the optimal satellite combination by evaluating geometric configurations [19]. Traditional methods include the maximum tetrahedral volume approach, the elevation angle threshold approach, and so on. [PERSON] et al. [20] utilized the principle of maximizing elevation angle and ensuring uniform azimuth angle distribution, thereby effectively reducing traversal time. [PERSON] et al. [21] achieved a uniform distribution of satellites by employing a method that involved grouping based on elevation angles and uniformly selecting azimuth angles. [PERSON] et al. [22] integrated the Sherman-Morrison formula with the maximum-volume tetrahedron method, thereby achieving an efficiency enhancement under the 11-satellite threshold. However,conventional satellite selection algorithms solely focus on geometric distribution while neglecting critical influencing factors such as signal strength, multipath error, and visible satellite duration. Additionally, the computational complexity grows exponentially with an increasing number of satellites, thereby imposing higher demands on the receiver's hardware capabilities and failing to satisfy real-time processing requirements. The intelligent optimization algorithm employs a heuristic global search strategy. By leveraging the population evolution mechanism, it successfully reduces algorithmic complexity, thereby significantly enhancing computational efficiency and diminishing the hardware requirements for the receiver. This algorithm is particularly well-suited for real-time positioning tasks that have low accuracy demands and utilize cost-effective receiver equipment. [PERSON] et al. [23] proposed a multi-constellation genetic algorithm (GA)-based satellite selection method, which avoids falling into local optima by preferentially selecting chromosomes during the crossover process. [PERSON] et al. [24; 25] successively introduced the Chaos Particle Swarm Optimization (CPSO) algorithm and the Artificial Fish Swarm Algorithm-Particle Swarm Optimization (AFSA-PSO) algorithm.