Integrated navigation method based on non-linear mapping self-adaptive hybrid Kalman/H infinite filters

Abstract

The invention discloses an integrated navigation method based on non-linear mapping self-adaptive hybrid Kalman / H infinite filters, which comprises the following steps of: establishing and describing a state equation and an observation equation of an integrated navigation system; 2, simultaneously running the Kalman filter and the H infinite filter in an integrated navigation hybrid filter; 3, obtaining the performance quantitative indicator of the Kalman filter; 4, establishing a nonlinear mapping relation between the performance quantitative indicator of the Kalman filter and the weighted parameter of the hybrid filter and adaptively adjusting weighted parameters; and 5, taking the weighted sum outputted by the Kalman filter and the H infinite filter as the whole hybrid filter as the whole hybrid filter to be outputted through the weighted parameters and finishing integrated navigation information processing. The integrated navigation method has the advantage that when the environmental noise and the system model interference are changed, higher filtering precision is obtained through automatic switching among Kalman filter state estimation, hybrid filter state estimation and H infinite filter state estimation.

Description

technical field [0001] The invention relates to a combined navigation method based on nonlinear mapping self-adaptive hybrid Kalman / H∞ filter, and belongs to the technical field of navigation of ships, airplanes, vehicles and the like. Background technique [0002] With the development of society and the advancement of human science and technology, people's requirements for navigation and positioning are getting higher and higher. It is necessary for the navigation and positioning system to provide high-precision navigation and positioning information continuously, in real time, and for a long time. However, due to its own shortcomings, a single navigation system cannot meet the requirements of modern navigation and positioning. For example, although the inertial navigation system has the ability of completely autonomous navigation, and the navigation information is comprehensive and concealed, but the navigation error accumulates over time, and it is difficult to provide ac...

AI Technical Summary

Problems solved by technology

These two robust hybrid H2 / H∞ filters have the following disadvantages. On the one hand, it is only suitable for linear time-invariant systems. On the other hand, it is conservative and has low precision.

The last category is the estimated weighted hybrid filter. At present, the weight of this type of hybrid filter is determined by the designer based on the experience of the inaccuracy of the system model and the inaccuracy of the statistical characteristics of the noise. The use of fixed weights makes the hybrid The method is very conservative, and when the Kalman filter diverges, this hybrid filter still diverges

The above method is not suitable for the practical application of integrated navigation system

Images