Initial alignment method on the basis of hypersphere sampling

Abstract

The invention discloses an initial alignment method on the basis of hypersphere sampling, which comprises the following steps of: step 1: establishing a nonlinearity state equation of the initial alignment of a SINS stationary base; step 2: establishing a strong tracking unscented filter method on the basis of hypersphere sampling to obtain a state variable estimated value; and step 3: collectingthe inertia output information of a track generator by a navigational computer and completing an alignment process. By adopting a hypersphere sampling strategy, the initial alignment method has the advantages of reducing the amount of sampling points and the calculated amount as well as the sampling time; and according to the initial alignment method, the strong tracking filter and the UKF (unscented Kalman filter) are combined and applied to the initial alignment of the strapdown inertial navigation stationary base, thereby solving the problem of reduction of filtering accuracy caused by system and uncertainty noise.

Description

technical field [0001] The invention relates to an initial alignment method based on hypersphere sampling, and belongs to the technical field of navigation. Background technique [0002] Initial alignment is a key technology to achieve high-precision inertial navigation. The accuracy of initial alignment directly affects the accuracy of the inertial navigation system. The alignment time is also an important tactical indicator that reflects the rapid response capability of the weapon system. The traditional initial alignment is based on the linear initial alignment error equation, introducing external measurement information, and using the Kalman filter algorithm to estimate the attitude error angle. Since the initial alignment is a practical problem determines the difficulty in practical operation. Such as: [0003] (1) The actual initial alignment process is a nonlinear process, and the corresponding error equation should also be a nonlinear model. The linear model is obt...

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Problems solved by technology

[0003] (1) The actual initial alignment process is a nonlinear process, and the corresponding error equation should also be a nonlinear model. The linear model is obtained by linearizing the nonlinear model under the premise that the error angle is small. When the quasi-angle becomes larger, the linear model will no longer be applicable, resulting in a decrease in filtering estimation accuracy

[0004] (2) In practical applications, when the noise statistics are inaccurate or wrong, the stability of the Kalman filter will decrease, the convergence speed will slow down, and even cause the filter to diverge. At this time, it is necessary to design a stronger tracking ability for the nonlinear model. filtering algorithm

[0006] UKF (Unscented Filter) has a large amount of calculation in the initial alignment of the SINS (Strapdown Inertial Navigation System) static base with a large azimuth misalignment angle. In the case of inaccurate or wrong noise statistics, the convergence speed becomes slower and the estimation accuracy decreases , and even filter divergence

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