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Application of improved UKF algorithm in initial alignment of SINS

Authors: WanXin Su;

Application of improved UKF algorithm in initial alignment of SINS

Abstract

In order to improve the initial alignment accuracy and convergence rate of the SINS system, proposed the improved UKF algorithm (AUKF) based on the Unscented Kalman Filter (UKF). Noise statistical characteristics are mostly unknown in real systems, when it was effected by the initial value errors and dynamic model errors, AUKF algorithm can real-time adjust the covariance of the state vector and observation vector, and balance the right ratio of the state information and observation information in the filter results, thereby improving the system performance. The experimental results show: The Improved UKF Algorithm enhances the convergence speed and alignment accuracy effectively.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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