SINS large-azimuth misalignment angle initial alignment method based on self-adaptation UPF
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A technology of initial alignment and misalignment angle, applied in the field of navigation
Inactive Publication Date: 2015-10-28
SOUTHEAST UNIV
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[0003] Purpose of the invention: the purpose of the present invention is to improve for most application environments that the initial misalignment angle is a large angle and the noise is Gaussian white noise and the deficiency of UPF. The present invention proposes an automatic tracking filter based on strong tracking filter technology A Method of Initial Alignment Adapting to UPF's Large Azimuth Misalignment Angle
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Embodiment 1
[0119] The SINS large azimuth misalignment angle initial alignment method based on adaptive UPF in this embodiment, the specific steps are:
[0120] Step 1: Establish a SINS nonlinear error model:
[0121] Use the Euler platform error angle to represent the misalignment angle between the ideal navigation coordinate system and the calculated navigation coordinate system, and the set of error angles needs to consider the order of rotation to establish a corresponding SINS nonlinear error model;
[0122] The coordinate system of the present invention is selected as follows:
[0123] i system - the earth center inertial coordinate system, the origin is at the center of the earth 0, x i Axis points to equinox, z i Axis along the Earth's rotation axis, y i axis and x i ,z i form the right-hand system;
[0124] e system——Earth coordinate system, the origin is at the center of the earth, x e axis passing through the intersection of the prime meridian and the equator, z e Axis ...
Embodiment
[0229] Under the condition of a static base, the constant drift of the gyroscope is 0.01° / h, and the random drift is 0.001° / h; the zero bias of the accelerometer is 100μg (g=9.8m 2 / s), the random deviation is 50 μg; the local geographic latitude is 32.37°, and the longitude is 118.22°. The simulation time is 2000s.
[0230] According to the large azimuth misalignment angle error model established, two kinds of filtering algorithms are used to carry out simulation experiments under the condition that the statistical characteristics of the noise are determined. Now select the initial misalignment angle Feedback correction is not performed during the simulation process, and the simulation results of the alignment error are as follows: figure 2 , image 3 and Figure 4 shown.
[0231] Table 1: Determining Noise Statistical Properties is the Alignment Statistical Results
[0232]
[0233] pass figure 2 , image 3 and Figure 4 It can be seen that the SINS error mode...
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Abstract
The invention discloses an SINS large-azimuth misalignment angle initial alignment method based on a self-adaptation UPF. The method includes the following steps that 1, an SINS non-linear error model is built; a misalignment angle between an ideal navigation coordinate system and a computed navigation coordinate system is presented by means of an Euler platform error angle, and the corresponding SINS non-linear error model is built according to the rotation sequence of the error angle. The method reduces influences caused by system simplification and noise statistic feature uncertainty on a system to some extent. Meanwhile, the UPF middle particle degradation phenomenon is well eliminated. Initial alignment precision of a strap-down inertial navigation system is improved, and alignment time is shortened. The method is a software method, system hardware does not need to be modified, and accordingly the method is convenient and feasible to practically implement.
Description
technical field [0001] The invention relates to a SINS large azimuth misalignment angle initial alignment method based on an adaptive UPF, and belongs to the technical field of navigation. Background technique [0002] With the continuous expansion of the navigation system application field, most application environments cannot meet the conditions that the initial misalignment angle is a large angle and the noise is Gaussian white noise. At this time, the traditional navigation system linearization model and KF (Kalman Filter, Kalman Filtering) will produce large model errors and estimation errors, making the navigation parameters unreliable. In view of this situation, domestic and foreign researches are mainly divided into two aspects: one is the research on the nonlinear model of the inertial navigation system, and the other is the research on the nonlinear filter. Commonly used nonlinear filtering methods are EKF (Extended Kalman Filter, Extended Kalman Filter), UKF (Uns...
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