Variable structure multi-model maneuvering target tracking method based on error-fuzzy decomposition

Abstract

The invention discloses a variable structure multi-model maneuvering target tracking method based on error-fuzzy decomposition, and the method comprises the steps: firstly employing an error ambiguitydecomposition (EAD) principle to derive an EAD-MSA (error ambiguity decomposition-model sequence set adaptation) standard, and enabling the square difference of the standard between a minimum estimation value and a real value to be optimal; therefore, an EAD-variable structure multi-model (EAD-VSMM) method is constructed. The method is clear in configuration structure and small in calculation amount, can be widely applied to the field of maneuvering target tracking, and can improve the precision and robustness of target tracking.

Description

technical field [0001] The invention relates to the field of maneuvering target tracking, and relates to an error-fuzzy decomposition variable structure multi-model maneuvering target tracking method. Background technique [0002] Although the single-model Bayesian target tracking method can be well applied to the problem of non-maneuvering target tracking. But the tracking problem of different motion models of maneuvering targets at different times is also very important, and these random maneuver models are usually unknown to the tracking method. Although we can use a single model Bayesian tracking method to track maneuvering targets, it may lead to tracking failure when the previous motion model does not match the real maneuvering pattern. [0003] To address the above issues, a multi-model approach has emerged, and the maneuvering motion is described by a hybrid system with discrete and continuous states. The key idea of ​​the multi-model approach is that if the target...

AI Technical Summary

Problems solved by technology

For the existing VSMM methods, the difference mainly lies in their different MSA mechanisms

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