A Rapid Satellite Selection Algorithm for BDS and GPS Dual Constellations Based on Ant Colony Optimization Clustering

Selecting an optimal subset of satellites is crucial for enhancing the performance of BDS/GPS dual-system satellite receivers. To address the low real-time performance of the traditional method, a rapid satellite selection algorithm based on Ant Colony Optimization Clustering (ACOC) is proposed. The satellite selection issue is treated as a combinatorial optimization problem, utilizing a clustering algorithm to establish a global model suitable for global locations. Simultaneously, Ant colony optimization and a local search strategy are employed to enhance the algorithm's efficiency in searching for the optimal subset. The experimental results demonstrate that the ACOC method efficiently identifies a highly accurate subset of satellites. Compared to the traditional method, it significantly reduces the time required for optimal satellite subset selection by 85% in a single epoch. Over multiple epochs, it consistently achieves a time reduction of over 90%, with 99% of epochs exhibiting a GDOP difference within 0.2. In comparison to the K-means++ method, the ACOC method improves accuracy standards by 51.15% when there are 31 visible satellites present. This approach effectively addresses limitations in existing algorithms and exhibits exceptional performance.