Salīdzināt metodes

	AHRS	GNSS RTK	INS kļūdu modelis
Nozare	Aviācija un kosmonautika	Aviācija un kosmonautika	Aviācija un kosmonautika
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	1940s	1980s	1960s
Autors	Aviation heritage	GPS constellation	Schuler and others
Tips	System	Positioning method	Stochastic model
Pirmavots	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.
Citi nosaukumi	AHRS system, attitude reference, heading sensor	RTK, Real-Time Kinematic positioning, GNSS-RTK, differential GNSS	INS error analysis, error state kalman filter, ESKF
Saistītās	3	3	3
Kopsavilkums	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.	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.