Integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning

Abstract

The invention discloses an integrated parameter estimation method for attitude and orbit of a spatial non-cooperative target based on depth learning. The parameter estimation algorithm models the kinematics and dynamics of the space non-cooperative targets by dual vector quaternion, and designs the corresponding state BP neural network parameter estimation algorithm and state covariance matrix convolution neural network parameter estimation algorithm on this basis. The attitude and orbit parameters of space non-cooperative targets are estimated integrally by using dual vector quaternion, and the attitude and orbit coupling effect of space non-cooperative targets is considered. At the same time, BP neural network with single hidden layer and convolution neural network with double hidden layers are designed to estimate the parameters of spatial non-cooperative targets under the condition of measurement failure, so that the parameter estimation algorithm has strong robustness to the spatial environment.

Description

technical field [0001] The invention belongs to the field of aerospace technology, and in particular relates to a method for estimating parameters of attitude-orbit integration of space non-cooperative targets based on deep learning. Background technique [0002] The ever-increasing amount of space debris has seriously affected normal human spaceflight activities. In particular, the growing number of failed spacecraft not only occupies a large amount of orbital resources, but also poses a great threat to space security. In recent years, major aerospace countries and international research institutions have reached a general consensus: in order to ensure the availability of orbital resources and the safety of space, it is necessary to remove space debris in space, especially failed spacecraft. During this process, due to failure, the spacecraft cannot provide its own information and rolls freely in space. Therefore, accurate parameter estimation is very important for subseq...

AI Technical Summary

Problems solved by technology

Under measurement failure conditions, if these parameters are estimated using traditional neural networks, it is prone to overfitting

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