Inverse matrix-free self-adaptive filtering-based SINS (strap-down inertial navigation)/DVL (Doppler velocity log) combined positioning method

Abstract

The invention relates to an inverse matrix-free self-adaptive filtering-based SINS (strap-down inertial navigation) / DVL (Doppler velocity log) combined positioning method and belongs to the technicalfield of high-precision SINS / DVL combined positioning. The invention aims to solve the problems of low calculation efficiency and poor stability of a traditional SINS / DVL combined navigation filteringalgorithm due to the complex calculation process of the algorithm. The inverse matrix-free self-adaptive filtering-based SINS (strap-down inertial navigation) / DVL (Doppler velocity log) combined positioning method of the invention includes the following steps that: corresponding state initial values and observation values are acquired based on the sensor information of a strap-down inertial navigation system and a Doppler velocity log; a corresponding system equation and observation equation based on a combined navigation error model are constructed, an inverse matrix-free self-adaptive filtering algorithm is adopted to correct errors, so that the post-correction speed and position error information of a target is obtained; and finally, the obtained error information and the observation information of the strap-down inertial navigation system and the Doppler velocity log are fused, so that a high-precision positioning result is obtained. With the method of the invention adopted, on the basis of decreasing a calculation amount and optimizing a calculation process, the reliability and stability of a system can be ensured, and the positioning precision of underwater autonomous navigation is improved.

Description

technical field

[0001] The invention relates to high-precision underwater positioning technology. Background technique

[0002] In the actual underwater positioning process, due to the complex and changeable underwater environment, the system noise and measurement noise of the strapdown inertial navigation and Doppler log combined positioning (SINS / DVL) system often have statistical characteristics. Certain time variability. In order to have a certain adaptive ability to the change of the statistical characteristics of the noise and improve the filtering accuracy, the adaptive Kalman filter algorithm is often used to process the noise. However, in the Kalman filtering process, most of the calculations are concentrated in the process of inversion operation. Moreover, the inversion operation is complicated for a computer and has poor reliability. Therefore, the present invention improves the adaptive Kalman filter algorithm by adopting the fast calculation matrix algorithm ...

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