评估GPS/BDS-3PPP-AR使用GA-BPNN方法预测的FCB与iGMAS产品
Jin Wang1,2, Guangyao Yang1, Qiong Liu3
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
使用基因算法优化逆向传播神经网络 (GA-BPNN) 的新预测模型准确预测宽带 (WL) 和窄带 (NL) 分数周期偏差 (FCB). 这提高了精确点定位 (PPP) 的准确性,并降低了计算负载.
科学领域:
- 地测和地质工程的工程.
- 卫星导航系统 卫星导航系统
背景情况:
- 精确点定位 (PPP) 依赖于模糊性分辨率 (AR) 的准确性.
- 增强现实需要宽带 (WL) 和窄带 (NL) 分数周期偏差 (FCB),通常来自计算密集的全球网络解决方案.
研究的目的:
- 开发一个有效的WL和NLFCB的预测模型.
- 评估预测FCB对PPP业绩的影响.
主要方法:
- 为FCB预测提出了一种基因算法优化逆向传播神经网络 (GA-BPNN).
- 将PPP与预测的FCB (BP-Fixed) 的性能与传统方法 (浮动,固定) 的性能进行比较.
主要成果:
- 实现了WL和NL FCB的高精度预测.
- 与iGMAS产品相比,预测的WL FCB偏差在0.01周期内,NL FCB在0.1周期内.
- 在BP-Fixed方案中,GPS+BDS-3.3的PPP精度为0.0034m (东),0.0039m (北) 和0.0100m (上).
- 实现了BP-Fixed计划的98.62%的模糊性固定利率.
结论:
- 该GA-BPNN模型提供准确和高效的FCB预测.
- 在PPP中使用预测的FCB可以获得与使用已有的FCB产品相比较的定位性能.
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