Nonlinear non-Gaussian ranking filtering method for Martian atmosphere entering section

Abstract

The invention discloses a nonlinear non-Gaussian ranking filtering method for a Martian atmosphere entering section. The method comprises four steps of 1, constructing an engineering actual equation based on a Martian atmosphere entering section detector; 2, supplying initial values: x0, P0 and statistical properties of system noise wk and vk, namely a mean value and a variance; 3, performing nonlinear non-Gaussian ranking method; 4, setting k=k+1, returning to the step 3 to continue the circulation until k is equal to the moment T corresponding to the end of the time of the Martian atmosphere entering section, namely the time when a supersonic parachute is opened, thus completing the nonlinear non-Gaussian ranking filtering method for the Martian atmosphere entering section. By the adoption of the nonlinear non-Gaussian ranking filtering method, the detector can reduce the influence, caused by non-Gaussian noise, on the estimation of a system state in the Martian atmosphere entering section, so that the state estimation precision can be improved, and the requirement of a Mars detection task in the future on the landing precision can be well met.

Description

technical field [0001] The invention relates to a nonlinear non-Gauss rank filtering method for the Martian atmosphere entry section. It belongs to the technical field of aerospace navigation. Background technique [0002] The Martian atmosphere entry stage is the stage where the entire Mars entry, descent, and landing stages last the longest, the aerodynamic environment is the most complex and harsh, the navigation error is the largest, and the stage that has the greatest impact on landing accuracy. In the future, Mars exploration needs to complete tasks such as sample return and manned landing with a landing error of no more than 1km or less than 100m, which puts forward higher requirements for the accuracy of autonomous navigation in the Martian atmosphere entry stage. The factors that affect the accuracy of autonomous navigation during the Martian atmosphere entry stage mainly include the following three aspects: [0003] 1. Accurate dynamic model of Mars atmosphere en...

AI Technical Summary

Problems solved by technology

However, in the entry section of the Martian atmosphere, the aerodynamic environment is complex and harsh, the dynamic model is strongly nonlinear, and the statistical characteristics of the noise are difficult to understand, so it is difficult to establish an accurate dynamic model

Therefore, in engineering practice, the noise statistical characteristics of the dynamic model are often assumed to be white noise that obeys the Gaussian distribution. Therefore, this assumption will bring potential navigation errors to the actual autonomous navigation of the Martian atmosphere entry stage

The traditional Kalman filtering method is only suitable for linear filtering problems in the case of Gaussian noise; the extended Kalman filtering method is mainly suitable for weak nonlinear systems, and there is a truncation error caused by linearization; and the unscented Kalman filtering method can be used for strong nonlinear systems system, but it is mainly used for nonlinear filtering of Gaussian noise systems

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