A Novel System-Level Calibration Method for Gimballed Platform IMU Using Optimal Estimation



An accurate calibration of inertial measurement unit errors is increasingly important
as the inertial navigation system requirements become more stringent.
Developing calibration methods that use as less as possible of IMU signals has
6-DOF gimballed IMU in space-stabilized mode is presented. It is considered
as held stationary in the test location incorporating 15 different error sources,
including accelerometers bias, scale factor error, gyros drift, initial alignment
error, and IMU case installation error. Using kinematic relations between IMU
platform, IMU body, and IMU platform centered inertial reference frame, six
differential equations of the only system-level IMU velocity and gimbal angle
are derived. Then the extracted model is validated for error-free case using Sim-
Mechanics MATLAB SIMULINK tools to evaluate the introduced mathematical
model. Simulation results for 24 hours point out the correctness of the developed
model in error-free case. The IMU error analysis methodology incorporates
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gimbal angle measurements taken during one and a half hour with 9 platform attitudes
test to estimate IMU error sources. Without the need to install IMU at rotating
table, different platform attitudes are achieved using consequent rotations
of gimbals. IMU error sources estimation is accomplished off-line. This paper
describes the design and test results of a new gimballed IMU calibration method
without using a rotating table, and error model development methodology formulated
to support the design and test of EKF algorithm and two optimal smoothers:
forward-backward and RTS. Results obtained from EKF implementation indicate
that the technique is comprehensive and accurate, and requires less specialized
test equipments. Also, results show that constant states are not smooth-able. Ad-