Strong tracking UKF filter method based on sampling point changing

Abstract

The invention provides a strong tracking UKF filter method based on sampling point changing. The strong tracking UKF filter method based on the sampling point changing comprises the steps that (1) initial parameter setting is carried out on a system; (2) Sigma points are sampled according to an orthogonal transformation sampling point method, a corresponding prediction equation is determined, and time and measurement are updated; (3) fading factors are calculated; (4) new one-step prediction covariance is calculated by using the fading factors, the Sigma points are recalculated, and auto-covariance and cross covariance after the fading factors are introduced are obtained through nonlinear measurement function propagation; (5) filter updating is carried out to the end. According to the strong tracking UKF filter method based on the sampling point changing, the problem of non-local sampling of the system is solved effectively, the precision of the system is improved, and the system is made to have certain strong tracking capability. The strong tracking UKF filter method based on the sampling point changing can be used for solving the problems of poor robustness and filtering divergence when a model of the system is uncertain, solves the problem of the non-local sampling in the high-dimensional system, and expands the application range of strong tracking filter. In an MEMS / GPS combined navigation system, the positioning and attitude determination performance of the MEMS / GPS combined navigation system can be improved through the method.

Description

technical field [0001] The present invention relates to a MEMS-INS / GPS integrated navigation attitude and parameter solution method for position information, especially an improved strong tracking unscented Kalman filter method, which is essentially a method based on transforming Sigma sampling points strong tracking filtering algorithm. Background technique [0002] With the pursuit of low-cost, light-weight, and high-precision navigation systems, micro inertial measurement unit (MEMS-IMU) and GPS combined systems have been widely used in the fields of guided weapons, ships, vehicles, and rocket guidance. The data resolution of the navigation system is the key link to determine the navigation accuracy, and the level of filtering accuracy will greatly affect the performance of the integrated navigation system. [0003] In the low-precision inertial navigation system, due to the large system error and complex situation, it is difficult to model the system accurately, and it ...

AI Technical Summary

Problems solved by technology

Although CKF effectively solves the problem of numerical instability of the UKF algorithm, it also introduces the problem of non-local sampling

The non-local sampling problem refers to the non-linear filtering algorithm based on the sampling point transfer mean and variance, if the sampling point used is far from the center point (mean value), it may cause a decrease in filtering accuracy

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