Inertial Navigation System Alignment Techniques

Inertial navigation systems (INS) rely on accelerometers and gyroscopes to track position and orientation without external references. In strapdown INS, the inertial measurement unit is affixed directly to the vehicle body, requiring an initial alignment process to establish the attitude matrix relating the body frame to the navigation frame. Alignment is typically divided into coarse and fine stages: coarse alignment leverages earth’s gravity and rotation vectors to estimate pitch and roll, while fine alignment refines heading and sensor biases using filtering techniques. In-motion alignment addresses scenarios where the platform is in operation, combining inertial data with auxiliary sensors such as odometers, Doppler velocity logs or satellite navigation. Key challenges include large initial misalignments, sensor noise and observability limitations under constrained manoeuvres. Advanced approaches employ adaptive and nonlinear filtering, quaternion-based error modelling and deliberate IMU rotations to improve robustness and convergence. These methods are essential for a broad spectrum of applications, from unmanned aerial and marine vehicles to autonomous cars and spacecraft, where precise orientation underpins navigation accuracy and mission success.

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A recent method for odometer-aided in-motion alignment derives a continuous nonlinear error model for a 16-state strapdown INS coupled with a vehicle odometer and introduces a backtracking extended Kalman filter with reverse navigation. This scheme substantially improves attitude estimation under large initial misalignment angles, offering rapid convergence during active manoeuvres. Another line of work focuses on initial alignment for unmanned vehicles by first denoising raw inertial sensor outputs through signal decomposition and neural-network-based algorithms, then applying a robust Huber-style filter to mitigate the effects of sensor nonlinearity and uncertainty. Field tests demonstrate enhanced stability and accuracy in both coarse and fine alignment stages. In airborne transfer alignment, researchers have proposed a velocity-and-attitude-matching framework for MEMS-based INS transfer, augmented by an adaptive incremental Kalman filter that adjusts to varying IMU error characteristics. This approach achieves shorter convergence times and improved bias estimation for gyroscopes and accelerometers, facilitating reliable mid-air alignment.

Inertial Navigation System Alignment Techniques publication trend

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

Technical terms

Strapdown Inertial Navigation System (SINS): An INS configuration where the inertial measurement unit is fixed to the vehicle, requiring computational attitude updates rather than mechanical gimbals.

Inertial Measurement Unit (IMU): A sensor assembly of accelerometers and gyroscopes that measures linear acceleration and angular velocity in the body frame.

Coarse alignment: The initial estimation of pitch and roll using gravity and earth-rotation vectors to provide a first approximation of the attitude matrix.

Fine alignment: The subsequent refinement of heading, sensor biases and attitude errors through filtering techniques under quasi-stationary conditions.

In-motion alignment: The process of determining attitude and sensor errors while the platform is in operation, often aided by external sensors such as odometers or Doppler velocity logs.

Observability: A system characteristic that indicates whether internal states, such as biases or attitude errors, can be inferred from available measurements.

Kalman filter (KF): A statistical estimator that recursively computes optimal state estimates in the presence of noise, with nonlinear variants such as the extended Kalman filter using linearisation.

References

1. Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles. IEEE Access (2019).
2. An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles. Sensors (2018).
3. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter. Sensors (2017).
4. Improving Observability of an Inertial System by Rotary Motions of an IMU. Sensors (2017).