基于帝国竞争优化的双目标综合决策选星算法

全球卫星导航系统（GNSS）的应用前景已经得到世界各国的普遍承认，其应用领域也趋于多样化，在此背景下，卫星接收机也要求其具有更快的解算速度和可靠的精度。针对目前多数接收机的选星算法都是固定选星数目从而限制算法机动性的问题，提出基于帝国竞争优化算法（ICA）的双目标综合决策选星算法。为了更好获取几何构型较好的卫星星座，引入可见卫星的卫星仰角和方向角先验信息，进行先验性约束，通过构建几何精度因子（GDOP）以及选星数目2个目标，进行综合决策的快速选星，提高了选星的灵活度，并且在满足用户精度的要求下减轻了多星座卫星接收机的计算负担。通过仿真实验和实测数据对双目标综合决策选星算法验证的结果表明:所提算法在高度截止角5°下引入先验性约束条件后平均选星数目在仿真数据和实测数据中缩减率分别为51.8%和45.4%，平均GDOP值较无约束下分别减少0.209 2和0.248 4。同时，所提算法单次选星平均耗时分别为0.168 4 s和0.303 1 s，与遍历法的选星耗时4 s相比，提高了95.79%和92.42%。 

Imperialist competitive optimized dual-objective comprehensive decision algorithm for satellite selection

With the development of Global Navigation Satellite System(GNSS), the prospect of GNSS has been widely recognized in the world. In particular, the positioning solutions with fast and accuracy calculation are essential for the GNSS receiver design. The most of the current satellite selection algorithms in the GNSS receiver fix the number of satellites in advance, which limits the performance of the algorithm. This paper proposes an Imperialist Competitive Algorithm (ICA) for satellite selection. In order to obtain better geometric configuration of satellite constellation, the prior information (elevation and azimuth of visible satellite) is introduced for prior constraint. The Geometric Dilution of Precision (GDOP) and number of satellites are two objectives of the optimization algorithm. Comprehensive decisions are used to quickly select satellites, making the selection of satellites more flexible, as well as reducing the computational burden of multi-constellation satellite receivers. Experiment results based on simulation and field data showed that, after priori constraints are introduced, at elevation angle 5°, the average number of satellites selected by the algorithm proposed in this paper is 51.8% of the maximum visible satellites based on simulation data and the average number of satellites is 45.4% of the maximum visible satellites based on field data. The average GDOP is decreased by 0.209 2 and 0.248 4 compared to the satellite selection without a priori constraint. At the same time, the average calculation time for once satellite selection is about 0.168 4 s and 0.303 1 s, with an improvement of 95.79% and 92.42% compared to the time consumption (i.e. 4 s) of the traversal method.