Global Navigation Satellite Systems (GNSS) aided Inertial Navigation System (INS) is a fundamental approach for attaining continuously available absolute vehicle position and full state estimates at high bandwidth. For transportation applications, stated accuracy specifications must be achieved, unless the navigation system can detect when it is violated. In urban environments, GNSS measurements are susceptible to outliers, which motivates the important problem of accommodating outliers while either achieving a performance specification or communicating that it is not feasible. Risk-Averse Performance-Specified (RAPS) is designed to optimally select measurements to address this problem. Existing RAPS approaches lack a method applicable to carrier phase measurements, which have the benefit of measurement errors at the centimeter level along with the challenge of being biased by integer ambiguities. This paper proposes a RAPS framework that combines Real-time Kinematic (RTK) in a tightly coupled INS for urban navigation applications. Experimental results demonstrate the effectiveness of this RAPS-INS-RTK framework, achieving 85.84% and 92.07% of horizontal and vertical errors less than 1.5 meters and 3 meters, respectively, using a smartphone-grade Inertial Measurement Unit (IMU) from a deep-urban dataset. This performance not only surpasses the Society of Automotive Engineers (SAE) requirements, but also shows a 10% improvement compared to traditional methods.

翻译：全球导航卫星系统（GNSS）辅助惯性导航系统（INS）是实现连续可用、高带宽的绝对车辆位置及完整状态估计的基本方法。对于交通运输应用，除非导航系统能够检测到违反规范的情况，否则必须达到规定的精度指标。在城市环境中，GNSS测量值易受异常值影响，这引出了一个重要问题：如何在满足性能指标或传达其不可行性的同时，有效容错异常值。风险规避性能指定（RAPS）方法旨在通过优化选择测量值来解决该问题。现有的RAPS方法缺乏适用于载波相位测量的技术，而载波相位测量虽具有厘米级测量误差的优势，却面临整数模糊度导致偏差的挑战。本文提出一种RAPS框架，将实时动态定位（RTK）技术融入紧耦合INS中，以适用于城市导航应用。实验结果表明，该RAPS-INS-RTK框架在深度城市数据集上，使用智能手机级惯性测量单元（IMU）实现了水平误差85.84%低于1.5米、垂直误差92.07%低于3米的性能。该表现不仅超越了美国汽车工程师学会（SAE）的标准要求，相较传统方法更提升了10%的性能。