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首页> 外文期刊>International Journal of Distributed Sensor Networks >A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou Navigation Satellite System/Global Positioning System receiver
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A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou Navigation Satellite System/Global Positioning System receiver

机译：基于自适应模拟退火粒子群优化的卫星选择算法北欧导航卫星系统/全球定位系统接收器

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In this article, we propose a new particle swarm optimization–based satellite selection algorithm for BeiDou Navigation Satellite System/Global Positioning System receiver, which aims to reduce the computational complexity of receivers under the multi-constellation Global Navigation Satellite System. The influences of the key parameters of the algorithm—such as the inertia weighting factor, acceleration coefficient, and population size—on the performance of the particle swarm optimization satellite selection algorithm are discussed herein. In addition, the algorithm is improved using the adaptive simulated annealing particle swarm optimization (ASAPSO) approach to prevent converging to a local minimum. The new approach takes advantage of the adaptive adjustment of the evolutionary parameters and particle velocity; thus, it improves the ability of the approach to escape local extrema. The theoretical derivations are discussed. The experiments are validated using 3-h real Global Navigation Satellite System observation data. The results show that in terms of the accuracy of the geometric dilution of precision error of the algorithm, the ASAPSO satellite selection algorithm is about 86% smaller than the greedy satellite selection algorithm, and about 80% is less than the geometric dilution of precision error of the particle swarm optimization satellite selection algorithm. In addition, the speed of selecting the minimum geometric dilution of precision value of satellites based on the ASAPSO algorithm is better than that of the traditional traversal algorithm and particle swarm optimization algorithm. Therefore, the proposed ASAPSO algorithm reduces the satellite selection time and improves the geometric dilution of precision using the selected satellite algorithm.
机译：在本文中，我们提出了一种新的粒子群卫星系统/全球定位系统接收器的基于粒子群优化的卫星选择算法，其目的是降低多星体全球导航卫星系统下接收器的计算复杂性。本文讨论了算法关键参数 - 例如惯性加权因子，加速度系数和群体大小对粒子群优化卫星选择算法的性能的影响。此外，使用自适应模拟退火粒子群优化（ASAPSO）方法改进了该算法，以防止会聚到局部最小值。新方法利用进化参数和颗粒速度的自适应调整;因此，它提高了逃避局部极值的方法的能力。讨论了理论衍生。使用3-H真实的全球导航卫星系统观察数据进行验证实验。结果表明，就算法精度误差的几何稀释的准确性而言，ASAPSO卫星选择算法比贪婪卫星选择算法小约86％，大约80％小于精度误差的几何稀释粒子群优化卫星选择算法。另外，基于ASAPSO算法选择卫星精度值的最小几何稀释的速度优于传统的遍历算法和粒子群优化算法的速度。因此，所提出的ASAPSO算法可减少卫星选择时间，并使用所选卫星算法改善精度的几何稀释。

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