A dvl-assisted sins robust initial alignment method between runs

Abstract

The invention discloses a DVL-assisted SINS robust on-moving initial alignment method. The DVL-assisted SINS robust on-moving initial alignment method comprises the following steps of step (1), acquiring real-time sensor data; step (2), establishing a vector apparent motion parameter equation; step (3), on the basis of establishing the vector apparent motion parameter equation, estimating a parameter matrix through adaptive robust Kalman filtering; step (4), establishing reconfiguration vectors based on estimated parameters; step (5), through an optimal base quaternion attitude determining method, computing the error angle between a determined attitude and a real attitude; step (6), setting an initial alignment moment as M and a real-time data acquiring moment as k, if k=M, outputting an initial alignment result and completing an initial alignment process, and if k<M, determining that the initial alignment process is not completed, and repeating the above steps of (1) to (5) until theinitial alignment process is completed. The DVL-assisted SINS robust on-moving initial alignment method solves the problems of poor alignment precision and divergent alignment processes when system measured noise is abnormal.

Description

Technical field: [0001] The invention relates to the field of initial alignment of a strapdown inertial navigation system, in particular to a DVL-assisted SINS robust travel-to-travel initial alignment method. Background technique: [0002] Strapdown inertial navigation system initial alignment technology is one of the key technologies for normal navigation and positioning of the system. Using DVL-assisted inertial navigation system for initial alignment during travel has the advantages of high alignment accuracy, good reliability, and strong autonomy. At present, the initial alignment can be divided into two processes: coarse alignment and fine alignment. The coarse alignment mainly realizes rough attitude estimation; the fine alignment criterion is to perform fine attitude estimation on the basis of rough alignment. Many scholars have conducted in-depth research on the initial alignment technology of SINS. In terms of fine alignment, the robustness of the alignment proces...

AI Technical Summary

Problems solved by technology

However, in terms of coarse alignment, the current research hotspots are all analytical methods based on the determination of the optimal base pose, and it is difficult to directly improve it through robust filtering techniques.

This also makes the rough alignment process susceptible to external auxiliary information noise interference, resulting in the disadvantage of poor stability of the alignment results, and the traditional method cannot perform robust coarse alignment

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