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Adaptive Kalman filtering for target tracking

Authors: Feng Xiao; Mingyu Song; Xin Guo; Fengxiang Ge;

Adaptive Kalman filtering for target tracking

Abstract

Kalman filtering is widely used in target tracking. However, conventional Kalman filtering may fail to track the target when there is acceleration, deceleration, and turn. In this paper, these maneuvers are characterized by two orthogonal components of the changed velocity in l 1 -norm. The adaptive factors to adjust the Kalman gain are then generated through a mapping function based on the characterization. Sea experimental results show that the proposed adaptive Kalman filtering is better at tracking the maneuvering target

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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!
5
Average
Average
Average
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