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首页> 外文期刊>Journal of Computers >A Parallel Particle Swarm Optimization Algorithm for Reference Stations Distribution
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A Parallel Particle Swarm Optimization Algorithm for Reference Stations Distribution

机译：参考站分布的并行粒子群优化算法

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Parallel Particle Swarm Optimization (PPSO) algorithm is proposed to optimize the reference stations distribution and this algorithm will increase the User Differential Range Error (UDRE) accuracy and enhance the flight safety. Due to the reference stations distribution largely influence the accuracy of UDRE, a concept of Satellite Surveillance Dilution of Precision (SSDOP) is used to reflect the effect of changing the reference stations distribution on UDRE. After analyzing the expressions of SSDOP and UDRE, UDRE is influenced by restriction factor and SSDOP when measurement noise is a certain value, and the restriction factor is independent on SSDOP. Then, a mathematical equation between SSDOP and UDRE is deduced from the SSDOP and UDRE expressions, and a linear trend is showed. A Particle Swarm Optimization (PSO) algorithm is proposed, and it first randomly generates a group of particles and each particle represents a reference stations distribution. The average SSDOP is used as the fitness function to evaluate each particle. Both the local best and global best are used to guide the search direction. However, the proposed PSO algorithm may converge too fast which makes the optimizing result to become the local optimization. Thus, the PPSO algorithm with parallel computing is proposed to overcome this problem. Experiments are made to compare the performance of the proposed PPSO algorithm, the proposed PSO algorithm, “ N-Angled ” method and Exhaustive Grid Search method. The proposed PPSO algorithm can find the best solution without falling in local optimization, and isn’t restricted by the state and amount of the satellites and the outline of the searching area.
机译：提出了并行粒子群算法（PPSO）来优化参考站的分布，提高用户差分距离误差（UDRE）的准确性，提高飞行安全性。由于参考站的分布在很大程度上影响UDRE的准确性，因此采用了卫星监视精度稀释（SSDOP）概念来反映更改参考站的分布对UDRE的影响。通过分析SSDOP和UDRE的表达式，当测量噪声为一定值时，UDRE受约束因子和SSDOP的影响，且约束因子与SSDOP无关。然后，根据SSDOP和UDRE表达式推导了SSDOP和UDRE之间的数学方程，并显示了线性趋势。提出了一种粒子群优化算法，该算法首先随机生成一组粒子，每个粒子代表一个参考站分布。将平均SSDOP用作适应度函数以评估每个粒子。本地最佳和全局最佳均用于指导搜索方向。然而，提出的PSO算法收敛速度太快，使得优化结果成为局部优化。因此，提出了一种具有并行计算功能的PPSO算法来克服这一问题。实验比较了所提出的PPSO算法，所提出的PSO算法，“ N角度”方法和穷举网格搜索方法的性能。提出的PPSO算法可以找到最佳解决方案，而不会受到局部优化的影响，并且不受卫星的状态和数量以及搜索区域轮廓的限制。

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