Initial inertial navigation alignment method based on terated strong tracking spherical simplex radial cubature Kalman filter (ISTSSRCKF)

Abstract

The invention discloses an initial alignment method based on an iterated strong tracking spherical simplex radial cubature Kalman filter (ISTSSRCKF). The method comprises the following steps: selecting the cubature point of a CKF by adopting an SSR rule; introducing a fading factor in a strong tracking filter into the time and measurement update equation of the CKF; and introducing a Gauss-Newton iterative algorithm and improving the corresponding information variance and covariance in the iterative process. According to the initial alignment problem under the conditions of large misalignment angle and base sway, the excessive sampling points in the high-order CKF are reduced during sampling by adopting the SSR rule, and the problem that the performance of the CKF is reduced when a model is inaccurate is solved by introducing a strong tracking algorithm. Latest measurement information is fully utilized in using the iterative process, so that the state estimation error is effectively reduced, and the initial alignment precision superior to standard CKF is obtained.

Description

technical field [0001] The present invention mainly relates to the field of navigation technology, in particular to an ISTSSRCKF-based inertial navigation initial alignment method. Background technique [0002] Before the inertial navigation system enters the working state, the initial alignment must be carried out. The strapdown inertial navigation system directly connects the inertial sensors—gyroscope and accelerometer to the carrier, and obtains a computing platform through calculation to replace the physical platform. Therefore, the strapdown inertial navigation system The initial alignment problem of the system is to solve the attitude matrix at the initial moment. SINS initial alignment directly affects the navigation performance of the strapdown system, that is, navigation accuracy and reaction time. Autonomous underwater vehicle (AUV) is a mission controller that integrates artificial intelligence and other advanced computing technologies. In order for an underwate...

AI Technical Summary

Problems solved by technology

In the actual application environment, especially under the conditions of large misalignment angle and severe shaking, it is impossible to use the classical Kalman filtering method with a linear small misalignment angle as the alignment model for filtering, and only a nonlinear alignment model can be established. Traditional nonlinear filtering algorithms such as EKF and UKF have the problems of low alignment accuracy and numerical instability in high-dimensional states, and have the disadvantages of poor ability to deal with uncertain factors. Therefore, SINS is invented to effectively deal with complex environments and has a certain strong tracking ability. Alignment method under non-linear shaking base is of great significance

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