Alignment method of an underwater large misalignment angle based on SINS (Strapdown Inertial Navigation System)/DVL (Doppler Velocity Log) of SRQKF (Square-root Quadrature Kalman Filter)

Abstract

The invention discloses an alignment method of an underwater large misalignment angle based on SINS (Strapdown Inertial Navigation System) / DVL (Doppler Velocity Log) of SRQKF (Square-root Quadrature Kalman Filter). The alignment method comprises the following steps: step 1: establishing a nonlinear error model and a nonlinear filtering equation of the SINS under the large misalignment angle; step 2: constructing the SRQKF by utilizing a multivariate Gauss point and coefficient configuration method and a square-root filtering method in Gauss-Hermite quadrature; and step 3: estimating the misalignment angle by utilizing the SRQKF, and correcting a strapdown attitude matrix, thus obtaining accurate strapdown attitude matrix and attitude angle. The alignment method disclosed by the invention has the advantage that the underwater alignment accuracy and alignment speed of the carrier strapdown system are improved.

Description

technical field [0001] The invention relates to the technical field of strapdown inertial navigation and integrated navigation, in particular to an SRQKF-based SINS / DVL underwater large misalignment angle alignment method. Background technique [0002] Initial alignment is related to the working accuracy of the inertial system, and is one of the key technologies in the strapdown inertial navigation system (SINS). Due to the complex sea conditions, the hull of the underwater vehicle is generally in a state of large shaking, so that the strapdown inertial navigation system cannot quickly complete self-alignment, and is in a state of large misalignment angle for a long time. In this case, it is necessary to use external information assistance to complete the initial alignment of the moving base and large misalignment angles. Underwater vehicles usually accomplish this task with the aid of Doppler Logs (DVL). [0003] However, since the hull is always in a state of large misal...

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Problems solved by technology

[0003] However, since the hull is always in a state of large misalignment angle and shaking, the conventional linear error model cannot accurately describe the error propagation characteristics of SINS. It is necessary to establish a more accurate nonlinear error model, and use the nonlinear filtering method to complete the initial alignment

[0004] In recent years, various nonlinear filtering methods - Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Volumetric Kalman Filter (CKF) have been continuously applied, but they all have some limitations. The accuracy of EKF is not high when the nonlinear characteristics are strong; although the accuracy of UKF and CKF has been improved, they cannot achieve high accuracy under complex conditions such as large misalignment angles, and the convergence speed is slow

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