방법 비교

	GNSS RTK	AHRS	INS 오차 모델
분야	항공우주공학	항공우주공학	항공우주공학
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1980s	1940s	1960s
창시자	GPS constellation	Aviation heritage	Schuler and others
유형	Positioning method	System	Stochastic model
원전	Teunissen, P. J. G., & Kleusberg, A. (Eds.). (2003). GPS for Geodesy (2nd ed.). Springer-Verlag.	Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.	Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.
별칭	RTK, Real-Time Kinematic positioning, GNSS-RTK, differential GNSS	AHRS system, attitude reference, heading sensor	INS error analysis, error state kalman filter, ESKF
관련	3	3	3
요약	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.	An Attitude Heading Reference System (AHRS) is a complete inertial navigation subsystem that estimates and outputs the three-dimensional orientation (attitude) and heading of a vehicle or platform. AHRS combines measurements from accelerometers, gyroscopes, and often magnetometers through sensor fusion algorithms (typically Kalman filters or complementary filters) to provide a drift-free, fast attitude estimate. AHRS is standard in aviation, marine navigation, and modern autonomous systems.	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.