System state estimation method based on improved nonlinear robust filtering algorithm

Abstract

The invention discloses a system state estimation method based on an improved nonlinear robust filtering algorithm and relates to the technical field of space navigation. The method is based on a Huber nonlinear regression algorithm, so that high filtering accuracy and stable filtering output can be obtained under the condition that a measuring output equation is nonlinear, and measuring noises are in non-Gaussian distribution.

Description

technical field [0001] The present invention relates to a method in the technical field of space navigation, in particular to a method for navigation, target tracking, fault detection and other fields, especially an improved nonlinear robust filtering algorithm applied to the relative navigation of distributed spacecraft system state estimation method. Background technique [0002] In distributed spacecraft relative navigation applications, the most commonly used navigation algorithm is the Kalman filter. When the relative position of the spacecraft is far away, GPS is generally used for relative position measurement. However, when the relative position of the spacecraft is relatively close, for example, ultra-close formation flight or the rendezvous and docking stage of two spacecraft, the reliability of GPS can no longer meet the needs of relative navigation measurement, and it is necessary to switch to radar or laser measurement system. According to relevant literature,...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention proposes a system state estimation method based on an improved nonlinear robust filtering algorithm, so that when the measurement output equation is nonlinear and the measurement noise is non-Gaussian distribution, a relatively High filtering accuracy and stable filtering output, can be applied in navigation, target tracking and fault detection and other fields

Images