方法对比

	航位推算	全球导航卫星系统实时动态（GNSS RTK）	惯性导航误差模型
领域	航空航天	航空航天	航空航天
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1940s	1980s	1960s
提出者	Maritime navigation tradition	GPS constellation	Schuler and others
类型	Navigation method	Positioning method	Stochastic model
开创性文献	Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.	Teunissen, P. J. G., & Kleusberg, A. (Eds.). (2003). GPS for Geodesy (2nd ed.). Springer-Verlag.	Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.
别名	ded reckoning, inertial navigation, odometry	RTK, Real-Time Kinematic positioning, GNSS-RTK, differential GNSS	INS error analysis, error state kalman filter, ESKF
相关	3	3	3
摘要	Dead Reckoning is a fundamental navigation method that estimates position and heading by integrating velocity and angular rate measurements from inertial sensors over time, without external references such as GPS. The term derives from maritime tradition ('deduced reckoning') and remains a cornerstone of aerospace and autonomous vehicle navigation. Dead reckoning works reliably in GPS-denied environments and is the baseline navigation method when external navigation aids are unavailable.	Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) is a high-precision positioning technique that uses carrier phase measurements from a reference receiver at a known location to correct the position estimates of a rover receiver in real time. Developed in the 1980s, RTK exploits spatial correlation of atmospheric errors to achieve centimeter-level accuracy within tens of kilometers of the reference station. RTK is now standard in surveying, construction, autonomous vehicles, and precision agriculture.	The INS Error Model is a mathematical framework that characterizes how errors in inertial sensor measurements propagate through a navigation system's estimates of position, velocity, and attitude. Developed during the 1960s and refined through decades of navigation research, the error model enables design of optimal estimation filters (e.g., Kalman filters) that fuse inertial measurements with external references (GNSS, LiDAR, cameras) to bound and correct accumulated errors. The error model is fundamental to understanding and improving inertial navigation performance.