基于并行遗传算法的高轨卫星导航选星方法

Satellite selection based on parallel genetic algorithm for high orbit autonomous satellite navigation

高轨航天器自主导航能力在北斗三号卫星导航系统建成后得到了增强,但是也带来了部分时刻可见星数量冗余的问题。为降低运算量以保证服务的实时性,提出一种利用多种群并行遗传算法(PGA)进行快速选择当前最优可见星组合的方法。该方法将加权精度因子(WDOP)作为适应度评判标准,利用粗粒度式并行划分成的多个子种群进行搜索加速,并通过变异因子差异化设置与子种群间的信息交流来提高搜索能力。对多个典型高轨环境下7颗及以上选星任务的仿真测试表明,基于PGA的选星方法解相比遍历法所求最优解绝对误差平均值小于0.1,相对误差最大不超过1%。仿真结果表明,在典型高轨环境F1接收机利用四系统组合导航时,所提方法可以有效地快速、准确完成指定卫星数的选星任务。

After the BeiDou-3 navigation satellite system was finished,the performance of high-orbit autonomous navigation was improved,but it also occasionally resulted in the redundancy of visible satellites.In order to reduce the arithmetic operations to ensure the real-time performance,based on a multiple-population parallel genetic algorithm(PGA),a new method to quickly select the optimal combination of visible satellites was proposed.The algorithm chooses the weighted dilution of precision(WDOP)as the fitness function,uses sub-populations in coarse-grained to speed up the search,and improves the searchability through the differential setting of mutation factors and the information exchange between sub-populations.The simulation experiments result of 7 or more satellite selection tasks in several typical high orbit environments show that the average absolute error between the PGA-based selection algorithm solution and the optimal solution obtained by the ergodic method is less than 0.1,and the maximum relative error is less than 1%.The outcomes demonstrate that,when the receiver employs the four-system integrated navigation in a typical high-orbit environment,the algorithm can efficiently execute the task of choosing satellites for the specified number of satellites fast and precisely.