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基于改进的狮子算法解决整数模糊性

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概括
此摘要是机器生成的。

一个增强的模拟化狮优化器 (SAALO) 算法提高了全球导航卫星系统 (GNSS) 整数模两可的分辨率. 与现有方法相比,SAALO在各种场景中表现出更快的解决速度和更高的成功率.

关键词:
在厘米级准确度的定位准确度.双差异载体阶段的双差异载体阶段.全球导航和定位系统全球导航和定位系统更高维度的模两可的解决方案.整数模两可的情况

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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
  • 优化算法 优化算法

背景情况:

  • 全球导航卫星系统 (GNSS) 依赖于双差异载波相观测来实现高精度定位.
  • 在GNSS模型中解决整数模两可是实现精确坐标的关键.
  • 现有的算法在有效和准确地解决这些模两可的问题上面临着挑战,特别是在高维的场景中.

研究的目的:

  • 为GNSS引入和评估一个增强的模拟化狮子优化器 (SAALO),用于双差异整数模两可的解析.
  • 评估SAALO在速度和成功率方面的表现,与既有算法对比.
  • 验证SAALO在高维和现实世界的短和中基线GNSS应用中的能力.

主要方法:

  • 开发一个增强的模拟化狮子优化器 (SAALO).
  • 对SAALO与狮优化算法 (ALO),LAMBDA和MLAMBDA进行比较分析,以解决速度和成功率.
  • 在6维和12维场景中进行高维整数模两可的解析测试.
  • 在使用单频GPS系统的短期和中期基线场景中实践应用测试.

主要成果:

  • 萨洛的溶解时间比兰巴和兰巴更快,分别为0.0496秒和0.01秒.
  • 在高维测试 (6D和12D) 中,SAALO的成功率超过了98%.
  • 在现实世界GPS测试中,SAALO在42.7公里基线上显示了略快的平均解决时间和5.2%更高的成功率,与LAMBDA相比.

结论:

  • 增强的SAALO算法对于GNSS整数模两可的解决方案是有效和可靠的.
  • 在速度和精度方面,SAALO提供了更好的性能,特别是在高维和实际应用中.
  • 该算法为提高GNSS定位精度提供了一个强大的解决方案.