An Inertial Navigation System (INS) is a self-contained navigation technology that determines position, velocity, and orientation by continuously measuring and integrating acceleration and rotation rate data from inertial sensors, accelerometers and gyroscopes, without requiring external references like GNSS satellites, ground-based beacons, or visual landmarks. This independence from external signals makes INS invaluable for navigation in environments where other positioning methods are unavailable or unreliable.

The fundamental principle of inertial navigation involves double integration of acceleration measurements to derive position change from a known starting point. Accelerometers measure specific force (acceleration) along three orthogonal axes, while gyroscopes measure rotation rates around those axes. A navigation processor continuously integrates these measurements: gyroscope data tracks how the sensor frame rotates relative to a reference frame (typically local-level north-east-down), enabling accelerometer measurements to be properly oriented; accelerometer data is then integrated once to obtain velocity and again to obtain position change.

INS technology spans a wide performance range depending on sensor quality. Navigation-grade systems using fiber optic or ring laser gyroscopes achieve exceptional accuracy, maintaining positioning errors below one nautical mile per hour of operation, suitable for aircraft, submarines, and spacecraft. Tactical-grade systems using high-quality MEMS sensors provide adequate performance for shorter missions or when periodically corrected by external references. Consumer-grade MEMS-based systems, while useful for short-term motion tracking, accumulate errors too rapidly for standalone navigation.

The most common modern application of INS technology is in integrated GNSS/INS systems, where inertial and satellite positioning complement each other. GNSS provides absolute position to bound INS drift errors, while INS provides continuous high-rate positioning through GNSS outages and improves dynamic tracking performance. This sensor fusion, typically implemented through Extended Kalman Filters, has become standard in automotive, aviation, robotics, and surveying applications, combining the absolute accuracy of satellite positioning with the autonomy and high bandwidth of inertial sensing.