Σύγκριση μεθόδων

	TERCOM	Αρχή Νεκρής Ναυτιλίας	Μοντέλο Σφάλματος INS
Πεδίο	Αεροδιαστημική	Αεροδιαστημική	Αεροδιαστημική
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1980s	1940s	1960s
Δημιουργός	Boeing, military guidance	Maritime navigation tradition	Schuler and others
Τύπος	Localization method	Navigation method	Stochastic model
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	TerCom, terrain-aided navigation, TANS	ded reckoning, inertial navigation, odometry	INS error analysis, error state kalman filter, ESKF
Συναφείς	3	3	3
Σύνοψη	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.	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.	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.