一种基于改进免疫算法的GNSS快速选星方法

A Fast GNSS Satellite Selection Method Based on Improved Immune Algorithm

针对多GNSS系统组合导航选星的实时性和可靠性需求,提出一种基于改进免疫算法的快速选星方法。该方法通过选取最优的迭代次数和初始化种群数目,可以在获取理想卫星几何结构的同时提高接收机运算效率。利用CUT0测站1 d实测数据对所提算法进行验证,结果表明,当初始化种群数目选在20附近、最大迭代次数选在120附近时,选星效果最好;在单BDS系统定位时,平均GDOP差值与遍历法的比值仅为0.57%,耗时比遍历法缩短53.27%;在GPS/BDS/Galileo/GLONASS/QZSS组合系统下,其平均GDOP与遍历法相差仅0.122,平均耗时(1.06 s)比遍历法缩短96.47%。该算法适用于多GNSS系统组合定位,具有良好的普适性。

Aiming at the real-time and reliability requirements of multi-GNSS navigation satellite selection,we propose a fast satellite selection method based on improved immune algorithm.By selecting the optimal iterations number and the initial population number,the ideal satellite geometry can be obtained and the operation efficiency of receiver can be improved.The 1 day measured data of CUT0 station is used to verify the proposed algorithm.The results show when the initial population number is around 20 and the maximum iterations number is around 120,the satellite selection effect is the best.In the single BDS system,the average geometric dilution of precision(GDOP)difference is only 0.57%compared to the traversal method,and the time consuming is reduced by 53.27%.In the GPS/BDS/Galileo/GLONASS/QZSS combined system,the average GDOP difference is only 0.122 and the average time consuming is 1.06 s,which is reduced by 96.47%compared to the traversal method.The algorithm is suitable for the multi-constellation selection,and has good universality.