Inertial Navigation System Algorithms and Applications
Summary
Inertial navigation systems (INS) employ measurements of linear acceleration and angular rate to compute position, velocity and orientation without reliance on external signals. Modern implementations use strapdown architectures in which accelerometers and gyroscopes are rigidly affixed to the vehicle, coupled with mechanisation algorithms to integrate sensor outputs in real time. Central challenges include compensating for sensor biases, noise and misalignment errors, as well as mitigating coning and sculling phenomena that arise from high-frequency motions. Advanced error-state Kalman filtering has become the de facto standard for fusing inertial data with aiding information, such as zero-velocity updates, odometry constraints or GNSS whenever available. Applications span aerospace and unmanned aerial systems, maritime and polar navigation, subterranean and mining equipment positioning, and unmanned underwater vehicles operating in GPS-denied environments. Recent research has emphasised novel rotation-modulation schemes, multi-sample compensation algorithms and adaptive data-fusion strategies to reduce drift to the sub-nanometre scale over extended durations. Ongoing advances in micro-electromechanical sensors, robust estimation techniques and motion-constraint models continue to extend the reach of inertial navigation into ever more demanding operational contexts.
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Inertial Navigation System Algorithms and Applications publication trend
The graph below shows the total number of articles in inertial navigation system algorithms and applications across all publications each year (not limited to Nature Index journals).
Technical terms
Strapdown Inertial Navigation System (SINS): An INS architecture in which sensors are fixed directly to the moving body, requiring computational mechanisation to resolve motion in three dimensions.
Inertial Measurement Unit (IMU): A package of accelerometers and gyroscopes that measures linear and angular motion.
Zero-Velocity Update (ZUPT): A technique that resets velocity estimates during detected stationarity to bound cumulative drift.
Kalman Filter: A recursive estimator that fuses inertial measurements with external observations to correct state estimates and reduce uncertainty.
Coning and Sculling Errors: High-frequency coupling errors in strapdown systems arising from rotational and translational vibrations.
Global Navigation Satellite System (GNSS): A constellation-based positioning service that provides absolute updates to aid inertial navigation when available.
References
- Simulation Optimization and Application of Shearer Strapdown Inertial Navigation System Modulation Scheme. Sensors (2023).
- Research on an Error Compensation Method of SINS of a Mine Monorail Crane. Energies (2023).
- INS/GNSS Integration for Aerobatic Flight Applications and Aircraft Motion Surveying. Sensors (2017).
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