用伪卫星提升弱信号下的定位精度，效果比传统方法好四成。
Loosely Coupled Factor Graph Optimization for Pseudolite-Augmented Navigation
- 将伪卫星与惯性数据松耦合融合，构建因子图优化框架
- 3D定位误差降低22.8%至41.3%，在低可见度场景下表现显著
- 适合室内外混合环境、卫星信号弱的高精度定位应用
在全局导航卫星系统（GNSS）信号受限环境中，伪卫星（PLs）可提供额外信号源以提升定位性能，但其在基于优化的框架中集成仍有限。本文提出一种松耦合因子图优化（FGO）框架，融合GNSS/PL最小二乘（LS）解算结果与惯性测量单元（IMU）数据。评估在仅可见四颗高仰角卫星、最多两台伪卫星发射机、持续80秒的低可见度场景下进行。相比标准LS方法，FGO实现22.8%至41.3%的平均3D误差降低；相较于GNSS-IMU基线，引入伪卫星进一步提升了定位精度，且性能受几何布局影响。
原文摘要 · Abstract (English)
In Global Navigation Satellite System (GNSS)-degraded environments, pseudolites (PLs) provide additional signal sources to enhance positioning performance, but their integration in optimization-based frameworks remains limited. This paper presents a loosely coupled factor graph optimization (FGO) framework that fuses the GNSS/PL least-squares (LS) solutions with inertial measurement unit (IMU) data. The evaluation considers low GNSS visibility scenarios with four high-elevation GNSS satellites and up to two PL transmitters over an 80~s window. FGO achieves a 22.8\% to 41.3\% reduction in mean 3D error compared to standard LS methods. Compared to a GNSS-IMU baseline, incorporating PL transmitters further improves positioning accuracy, with performance depending on geometry.
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