基于自适应性遗传粒子群优化算法的单频GNSS整数模糊性解决方法
Ying-Qing Guo1, Yan Zhang1, Zhao-Dong Xu2
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一种自适应性遗传粒子群优化 (AGPSO) 算法,通过提高整数模糊性分辨率,实现更快,更准确的单频全球导航卫星系统 (GNSS) 定位.
科学领域:
- 地理学工程 工程地质学
- 卫星导航系统 卫星导航系统
- 人工智能在定位方面的作用
背景情况:
- 使用全球导航卫星系统 (GNSS) 准确的定位在很大程度上依赖于解决载波阶段整数模两可.
- 解决整数模两可的传统方法可能是低效和缓慢的,阻碍了快速定位.
研究的目的:
- 开发一种新的算法,用于高效和准确的单频GNSS整数模两可的解决方案.
- 通过智能优化技术,提高模两可的搜索过程的速度和稳定性.
主要方法:
- 为单频GNSS提出了一个自适应性遗传粒子群优化 (AGPSO) 算法.
- 使用载体相双差方程用于浮点解决方案和共变矩阵估计.
- 采用逆整数乔莱斯基算法来处理关系,并改进了健身功能以提高性能.
- 集成的粒子群优化与适应性权重,交叉和突变,用于强大的整数模糊性搜索.
主要成果:
- 与传统和其他智能算法相比,AGPSO算法显示了更快的融合率.
- 在整数模糊性搜索结果中实现了更好的稳定性.
- 实践实验表明,对于短的基线,基线精度在0.02米以内.
结论:
- AGPSO算法为单频GNSS整数模两可解决方案的效率和准确性提供了显著的改进.
- 该方法显示了实际应用价值,特别是在短期基线场景中.
- 开发的算法有效地解决了现有方法在速度和稳定性的局限性.
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