方法对比

	地形等高线匹配 (TERCOM)	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	1980s	1980s	1960s
提出者	Boeing, military guidance	GPS constellation	Schuler and others
类型	Localization method	Positioning method	Stochastic model
开创性文献	Golden, J. P. (1983). Terrain contour matching (TERCOM): A cruise missile guidance aid. In In-Flight Measurement Technology. AGARD Conference Proceedings No. 336, 3–1 to 3–16.	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	TerCom, terrain-aided navigation, TANS	RTK, Real-Time Kinematic positioning, GNSS-RTK, differential GNSS	INS error analysis, error state kalman filter, ESKF
相关	3	3	3
摘要	Terrain Contour Matching (TERCOM) is a terrain-aided navigation method that corrects position estimates by matching altimeter measurements against a stored digital elevation map (DEM). Developed by Boeing in the 1980s for cruise missile guidance, TERCOM enables accurate navigation in GPS-denied environments by exploiting the unique terrain signature at each location. TERCOM remains essential for missile guidance, autonomous underwater vehicles, and systems operating in jamming scenarios.	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.