Space-target positioning method based on multi-model filtering

Abstract

The invention discloses a space-target positioning method based on multi-model filtering. The space-target positioning method comprises the following steps: firstly, selecting the position and the velocity vector of a space target as state variables, selecting different system noise variance matrixes, and establishing a plurality of models for describing the uncertainty of the orbital maneuver acceleration of the space target; secondly, establishing a plurality of parallel-extension Kalman filtering algorithms for the plurality of models, and respectively carrying out filtering to obtain a plurality of state estimated values; thirdly, calculating corresponding filtering weights according to the conforming degree of all the state estimated values and observed quantities; and finally, calculating the weighted sum of the state estimated values of all the parallel-extension Kalman filtering algorithms according to the filtering weights to obtain navigation filtering results, namely the estimated values of the position and the speed vector of the space target. The space-target positioning method disclosed by the invention has the advantages that the system noise variance matrixes playing a leading role can be selected in a self-adaptive manner, so that the tracking and positioning capabilities of a filter to the maneuver target is enhanced, and the space-target positioning method is favorable for solving the problem that the uncertainty of the maneuver acceleration influences the positioning accuracy of the space target.

Description

technical field [0001] The invention relates to a space target positioning method based on multi-model filtering, and belongs to the technical field of aerospace control. Background technique [0002] Accurately obtaining and tracking the relative positions of spacecraft and space targets is of great significance for space missions such as rendezvous and docking or on-orbit operations; for high-value satellite platforms, determining the position of space targets or space junk is the key to avoiding collisions of spacecraft premise. Star camera photographic observation is one of the effective measurement methods for long-distance space target positioning. The star camera installed on the tracking spacecraft can accurately measure the line-of-sight vector of the space target relative to the tracking spacecraft. For non-cooperative targets, it is impossible to establish an inter-satellite link between the tracking spacecraft and the space target, and the measurement range of i...

AI Technical Summary

Problems solved by technology

However, when the space target has orbital maneuvering capability, there will be unknown interference in the dynamic equation, and the orbital maneuvering acceleration and maneuvering time of the space target are unknown

Unknown interference can be regarded as model uncertainty. When there is uncertainty in the system model, the KF algorithm is not optimal; when the influence of model uncertainty is significant, it will lead to an increase in the estimation error of the KF algorithm. poor performance

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