Calibration Techniques in Inertial Navigation Systems

Calibration lies at the core of ensuring precise performance in inertial navigation systems (INS), where minute errors in gyroscopes and accelerometers propagate into significant navigational inaccuracies over time. Modern calibration strategies address sensor biases, scale factor deviations, misalignments and environmental effects through a blend of static and dynamic methods. Static turntable approaches establish baseline error models under controlled orientations, while rotational modulation schemes deliberately spin the inertial measurement unit (IMU) about one or more axes to convert constant biases into oscillatory signals that can be distinguished and removed. System-level calibration employs high-dimensional error-state estimation, often realised via Kalman filtering and smoother algorithms, to jointly identify and compensate multiple error sources, including cross-coupling and lever arm effects. Temperature-dependent drifts are increasingly tackled through thermal-chamber procedures and expanded error models incorporating temperature coefficients. Self-calibration techniques embed the calibration process within normal vehicle manoeuvres or at initial alignment, reducing reliance on specialised equipment. These techniques underpin high-performance navigation in GPS-denied environments across aerospace, maritime and land applications, and are instrumental for emerging ultra-high-precision devices such as atomic gyroscope-based IMUs.

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Recent advances have demonstrated the value of hybrid and multi-axis rotation schemes to enhance calibration precision. A dual-inertial navigation framework combines a strapdown system with a dual-axis rotating INS and an optimally tuned Kalman filter, yielding reductions of over 35 % in pitch error and 45 % in roll error by exploiting complementary error dynamics. Thermal effects have been tackled by integrating temperature-related coefficients into a system-level calibration protocol: an 18-step procedure in a thermal chamber paired with a 42-state Kalman filter delivered a 30 % decrease in pure inertial positioning error after compensation. For next-generation ultra-high-accuracy IMUs, a systematic calibration model incorporating gyro g-sensitivity, accelerometer cross-coupling and lever arm errors was proposed. By employing a 51-state Kalman filter and smoothing algorithm on a common dual-axis turntable, all small-order error parameters could be estimated, leading to an 8 % improvement in five-day navigation accuracy and potential gains exceeding 20 % for atomic gyro systems. These studies underscore the interplay between advanced estimation techniques, expanded error modelling and practical platform constraints.

Calibration Techniques in Inertial Navigation Systems publication trend

The graph below shows the total number of articles in calibration techniques in inertial navigation systems across all publications each year (not limited to Nature Index journals).

Technical terms

Inertial Measurement Unit (IMU): A module containing gyroscopes and accelerometers that measures rotational rates and linear accelerations.

Bias: A constant offset in a sensor output that causes systematic drift if uncorrected.

Scale Factor: The coefficient that relates the true physical input to the sensor’s output; deviations lead to proportional errors.

Misalignment: Angular deviation between the sensor axes and the reference axes, introducing cross-coupling of motion components.

Rotation Modulation: A calibration technique in which the IMU is deliberately rotated to convert static errors into time-varying signals for separation and estimation.

Kalman Filter: A recursive algorithm that estimates system states and error parameters by combining dynamic models with noisy measurements.

References

1. A Combination Scheme of Pure Strapdown and Dual-Axis Rotation Inertial Navigation Systems. Sensors (2023).
2. Systematic Calibration for Ultra-High Accuracy Inertial Measurement Units. Sensors (2016).
3. A system-level calibration method including temperature-related error coefficients for a strapdown inertial navigation system. Measurement Science and Technology (2021).