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An improved satellite selection algorthm based on fuzzy comprehensive evaluation method and the entropy method for determining the weight of evaluation indicators

机译:基于模糊综合评价法和熵权法的评价指标权重改进卫星选择算法

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In Global Navigation Satellite Systems (GNSS), the satellite selection algorithm is very important for increasing navigation location precision, in which visible satellite geometry is the most crucial factor. There are two algorithms for selecting stars: one is by calculating GDOP such as classical optimal algorithm, best GDOP algorithm and so on. However, it requires a long time for the computing procedure and must reselect stars to compute every 15 minutes. By comparison, the other is fuzzy selecting star algorithm, which has an advantage on a shorter computing time and almost the same performance as classical optimal algorithm. But it refers to the determination of weight of evaluating indicators which always takes the experience value but doesn't have the religious mathematical model as a support. Based on fuzzy selecting star algorithm, this paper proposes an improved algorithm by using the Entropy method for determination of weight of evaluating indicators. Simulation results indicate that the algorithm in this paper has almost the same performance as the previous algorithm, but less time-consuming and better position accuracy.
机译:在全球导航卫星系统(GNSS)中,卫星选择算法对于提高导航定位精度非常重要,其中可见卫星的几何形状是最关键的因素。选择星星的算法有两种:一种是通过计算GDOP来进行的,例如经典最优算法,最佳GDOP算法等。但是,这需要很长的时间进行计算,并且必须每15分钟重新选择星星进行一次计算。相比之下,另一种是模糊选择星算法,它具有计算时间短,性能几乎与经典优化算法相同的优点。但是它是指评估指标权重的确定,该指标总是采用经验值,却没有宗教数学模型的支持。在模糊选择星算法的基础上,提出了一种基于熵的改进算法,用于评价指标权重的确定。仿真结果表明,该算法与以前的算法具有几乎相同的性能,但耗时少,定位精度高。

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