用遗传算法优化低地球轨道卫星星座的可见性,以提高导航潜力
Chao Qin1,2, Yanbin Gao3, Yihuan Wang4
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China. max_qinchao@163.com.
Scientific reports
|August 21, 2025
概括
这项研究使用适应式并行遗传算法 (GA) 提高了低地球轨道卫星星座的可见性. 优化的框架提高了导航准确性和动态稳定性,优于其他方法.
科学领域:
- 卫星星座的设计
- 优化算法
- 轨道力学
背景情况:
- 低地球轨道 (LEO) 卫星星座面临着可见性优化的挑战.
- 确保导航准确性和动态稳定性对于低水平星座至关重要.
- J2 扰动效应会影响恒星的长期性能.
研究的目的:
- 提出一个改进的框架来优化LEO卫星星座的可见性.
- 提高卫星星座的导航精度和动态稳定性.
- 为下一代全球导航卫星系统 (GNSS) 提供技术基础.
主要方法:
- 开发了一个可适应的并行遗传算法 (GA) 框架.
- 设计了一个混合星座, 集成极, 步行者和直角圆形轨道.
- 包含一个动态放松因子 (γ) 来补偿J2扰动.
- 使用种群多样性进行适应性参数调整.
- 使用了多核架构的并行适应性评估策略.
- 采用简化的健身功能设计.
主要成果:
- 在100颗卫星的情况中平均达到14.3颗可见卫星,超过D-NSDE的3.6%.
- 将位置精度稀释 (PDOP) 降至2.3,全球覆盖率为95.6%.
- 在单个卫星故障后,覆盖率仅下降3.5%.
- 在10年的轨道扰动后, 保持94.8%的覆盖率.
- 在500-1000个卫星场景中,达到PDOP≤2.8的趋同时间低于210秒.
- 在可见性,强度和效率方面表现优于粒子群优化和动态非主导排序差异演变.
结论:
- 拟议的自适应平行 GA 框架有效优化了 LEO 卫星星座的可见性.
- 该框架提供了卓越的导航精度,动态稳定性和计算效率.
- 这项研究为未来的GNSS开发提供了重大进展.
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