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A Satellite Selection Algorithm Based on PSO for a Integrated Navigation Receiver

机译:组合导航接收机的基于PSO的卫星选择算法

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Multi-constellation integrated navigation receiver will increase the number of visible satellites and improve positioning accuracy of the receiver. If all-in-view satellites are used for receiver positioning, the computational burden of the receiver will be increased. The traditional satellite selection algorithm is traversal algorithm; however, as the number of visible satellites increases, the traversal algorithm exists huge computation. In order to the problem, the improved particle swarm optimization (PSO) is given for satellite selection, in the proposed algorithm, each satellite subset is considered a particle without mass in search space, and the selected objective function is the geometric dilution of precision (GDOP). Particles update their position based on the proposed algorithm model. Moreover, the optimal satellite subset and the corresponding GDOP value are obtained. The performance of the algorithms is compared based on real navigation data. The simulation results show that proposed algorithm can improve satellite selection speed, and the satellite selection accuracy is better than that of the basic PSO satellite selection algorithm.
机译:多星座集成导航接收机将增加可见卫星的数量,并提高接收机的定位精度。如果将全视角卫星用于接收器定位,则将增加接收器的计算负担。传统的卫星选择算法是遍历算法;然而,随着可见卫星数量的增加,遍历算法存在着巨大的计算量。针对该问题,提出了一种改进的粒子群优化算法(PSO)进行卫星选择,该算法将每个卫星子集视为搜索空间中没有质量的粒子,选择的目标函数是精度的几何稀释度( GDOP)。粒子根据提出的算法模型更新其位置。此外,获得了最佳卫星子集和相应的GDOP值。基于真实导航数据比较算法的性能。仿真结果表明,该算法能提高选星速度,且选星精度优于基本的PSO选星算法。

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