Low-cost sensor navigation has risen in the past decade with the onset of many modern applications that demand decimeter-level accuracy using mass-market sensors. The key advantage of the precise pointing positioning (PPP) technique over real-time kinematic (RTK) is the non-requirement of local infrastructure and still being able to attain decimeter to sub-meter level accuracy while using mass-market low-cost sensors. Achieving decimeter to sub-meter-level accuracy is a challenge in urban environments. Therefore, adaptive filtering for low-cost sensors is necessary along with motion-based constraining and atmosphere constraints. The traditional robust adaptive Kalman filter (RAKF) uses empirical limits that are derived by analyzing the GNSS receiver data learning statistics based on confidence intervals beforehand to determine when the adaptive factor needs to be applied. In this research, a new technique is proposed to determine the adaptive factor computation based on the detection of an increase in the number of satellite signals after a partial outage. The proposed method provides 6–46% better accuracy than the traditional RAKF and 11–55% better accuracy performance when compared to a tightly coupled solution without enhancements when multiple datasets were tested. The results prove to be a significant improvement for the next generation of applications, such as low-autonomous and intelligent transportation systems.

过去十年中，随着许多需要使用大众市场传感器达到分米级精度的现代应用的出现，低成本传感器导航不断兴起。精确指向定位 (PPP) 技术相对于实时动态 (RTK) 技术的主要优势是不需要本地基础设施，并且在使用大众市场低成本传感器的同时仍然能够达到分米到亚米级的精度。在城市环境中实现分米到亚米级的精度是一个挑战。因此，低成本传感器的自适应滤波以及基于运动的约束和大气约束是必要的。传统的鲁棒自适应卡尔曼滤波器（RAKF）使用经验限制，这些经验限制是通过预先分析基于置信区间的 GNSS 接收器数据学习统计数据而得出的，以确定何时需要应用自适应因子。在这项研究中，提出了一种新技术，根据部分中断后卫星信号数量增加的检测来确定自适应因子计算。在测试多个数据集时，与没有增强功能的紧耦合解决方案相比，所提出的方法比传统 RAKF 的精度提高了 6-46%，精度性能提高了 11-55%。结果证明，对于下一代应用程序（例如低自主性和智能交通系统）来说是一个重大改进。