摘要
全球卫星导航系统(GNSS)接收机在接收信号的过程中会受到诸如建筑物遮挡、信号干扰等因素的影响,无法得到全部可见星。为减轻多星座组合接收机的处理负担,研究利用部分可见卫星进行定位的快速选星算法,提出了一种基于混沌粒子群优化(CPSO)的北斗/GPS组合导航选星算法。首先,对当前历元时刻可见卫星进行连续编码,按照选星数目分组,每个分组视为一个粒子。然后,通过混沌映射初始化粒子种群,选取几何精度因子(GDOP)作为评价粒子优劣的适应度函数;粒子通过粒子群优化算法的速度-位移模型更新自身位置,逐渐趋近空间卫星几何分布较好的卫星组合全局最优解。最后,采集北斗/GPS实际数据对选星算法进行仿真验证和性能比较,结果表明,所提算法在选星颗数多于5颗时,单次选星耗时为遍历法选星的37. 5%,选星结果的几何精度因子计算误差在0～0. 6之间。该算法可适用于北斗/GPS组合导航定位不同选星颗数的情况。
In the process of signal receiving,global navigation satellite system(GNSS) receiver will be affected by factors such as building blockages and signal interference and will not be able to obtain all the visible satellites;moreover,in order to reduce the processing burden of multi-constellation receivers,the fast satellite selection algorithm using partial visible satellites to achieve positioning solution is investigated,and the BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization(CPSO) is proposed.First,the visible satellites are continuously numbered and randomly divided into groups.Each group is regarded as a particle.Then,chaotic maps are used to select several groups from all grouping spaces to form initial population.The geometric dilution of precision(GDOP) is chosen as fitness function to evaluate the particle's quality.In addition,the particle's position is updated by the velocity-displacement model of the PSO algorithm,and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite.Finally,using real navigation data,the algorithm is verified by simulation experiments.The results demonstrate that when the number of selected satellite is more than 5,the time that the proposed algorithm takes to select satellite once is 37.5% of the time that the traversing algorithm takes,and the GDOP error of the selected satellites is between 0 and 0.6.Moreover,the proposed algorithm can be applied to the case of different numbers of selected satellite in BDS/GPS integrated navigation.
引文
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