• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.351-358
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• 2006

The proposed inertial measurement unit (IMU) is composed of accelerometers only. It can determine a vehicle's position and attitude, which is the Gyro-free INS. The Gyro-free INS error is deeply affected by the sensor bias, scale factor and misalignment. However, these parameters can be obtained in the laboratory. After these misalignments are corrected, the Gyro-free strap-down INS could be more accurate. This paper presents a compensator design for the strap-down six-accelerometer INS to correct misalignment. A calibration experiment is taken to get the error parameters. A simulation results show that it will decrease the INS error to enhance the performance after compensation.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.2
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• pp.91-103
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• 2018

Conventional inertial measurement units cannot be used in the spinning vehicle with high rotation rate due to gyro's narrow operation range. By the way, angular acceleration can be measured using the accelerometer array distributed in the vehicle. This paper derives a mechanization for the gyro-free INS in the navigation frame, and proposes a gyro-free INS algorithm based on the derived mechanization. In addition, the proposed algorithm is used to estimate angular velocity, attitude, velocity, and position of a spinning vehicle with high rotation rate. A MATLAB-based software platform is configured in order to show validation of the proposed algorithm. The reference trajectory of a spinning vehicle at 3 round per second, 30 round per second are set up, and the outputs of accelerometer are generated when triads of accelerometer are located at the origin and all the axes. Navigation results of the proposed algorithm for the generated output are presented. The results show that the proposed navigation algorithm can be applied to the spinning vehicle with high rotation rate.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.3
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• pp.107-117
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• 2019

When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.