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This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

The technology of rapid transfer alignment for inertial navigation system with small attitude errors has been adequately studied in theoretical research and widely applied in engineering practice, satisfying the requirements of rapidity and high degree of accuracy [

Obviously both of the two models have their own shortcomings. In this paper, we aim to find a transfer alignment algorithm appropriate for arbitrary misalignment angles, which has the advantages of fast convergent rate and high accuracy. A twice transfer alignment algorithm based on dual models is proposed in this paper, which combines the advantages of both nonlinear and linear error models.

The quaternion is widely used as an effective method to compute the attitude of INS (inertial navigation system). According to the relationship between quaternion errors and tilt angle, quaternion errors can be used to describe the attitude error. The quaternion and velocity error differential equation can be given as follows:

The simulation is performed in two different cases. In Case A, model based quaternion and EKF which is easily realized in engineering project are adopted in the first phase. Through analyzing the performance of the nonlinear system by simulation experiments, the time point to switch to linear error model and classical Kalman filter in the next stage is decided at the 10th second. In Case B, model based additive quaternion and EKF are utilized. The initial attitude errors in roll, pitch, and heading are all assumed to be 20°. The total time of the simulation is 60 s; the initial position is set at 32.5 degrees north latitude, 135 degrees east longitude, 500 meters height; the initial attitudes are all set at 0°; the initial north velocity is 50 m/s; east and vertical velocities are 0 m/s. The constant gyrodrifts and accelerometer biases are set at 10°/h, 5 mg, respectively. The simulation results are shown in Figures

Alignment accuracy comparison.

Estimated error of attitude (°) | |||
---|---|---|---|

Pitch | Roll | Yaw | |

Case A | 0.002 | 0.001 | 0.005 |

Case B | 0.12 | 0.22 | 0.18 |

The attitude estimation error of first phase.

The attitude estimation error of second phase.

Figure

Figure

In order to verify the performance of the proposed algorithm, vehicle tests are conducted [

SINS and MINS of the vehicle test.

Curve of the vehicle results.

In Figure

Figure

In order to satisfy the requirements of rapidity and high accuracy in transfer alignment with large uncertainty, a transfer alignment algorithm based on dual model is presented. The simulation and vehicle test results demonstrate that this method could combine the advantages of nonlinear and linear error models which not only can be used in inertial navigation system with large uncertainty but also has the same high accuracy as the linear system.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was supported by NSAF (Grant no. U1330133) and the Natural Science Foundation of Jiangsu Province (Grant BK20130774).