Optimal Clustering of GPS Satellites Set using Modified ACO Algorithm

The Geometric Dilution of Precision (GDOP) is an important factor that demonstrates the effectiveness of the GPS satellite geometry on the positional navigation performance. The matrix inversion conventional method for GPS GDOP clustering has a large amount of operation, which would be a burden for real-time application. In this paper a new approach based on Modified Ant Colony Optimization (MACO) algorithm for satellites geometry clustering is proposed. The proposed method applies different way for update pheromone which uses information of local and global searches. The MACO-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. Simulation results indicate the proposed method has more performance to converge upon the optimal value in the GPS GDOP clustering.