A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

Selecting the optimal satellite subset for positioning from all satellites in view can not only achieve positioning accuracy but also reduce the computational burden. In this article, a navigation satellites selection method is proposed based on ant colony optimization with the improvement of polarized feedback (ACO-PF). Firstly, the satellite selection problem is described as a combinatorial optimization problem, and the noise weighted geometric dilution of precision (NWGDOP) is defined as the criterion for satellite selection. Then the ant colony optimization (ACO) is incorporated to solve the problem, and a polarized feedback mechanism is presented to improve the convergence speed of algorithm. Meanwhile, a perturbation operator is designed to improve the global searching ability of the algorithm. The numerical experimental results show that ACO-PF can select the superior satellites combination which provides high-precision positioning. And its convergence outperforms the related algorithms by up to 50%. Besides, the achieved NWGDOP of ACO-PF is usually 0.065 smaller than ACO. Therefore, the ACO-PF method can be considered as a promising candidate for satellite selecting in navigation applications.