Improved adaptive unscented Kalman filter algorithm for target tracking

Improved adaptive unscented Kalman filter algorithm for target tracking

An adaptive unscented Kalman filter (AUKF) algorithm is proposed to solve the problem that the statistical characteristics of the process noise are unknown in the target tracking, which leads to filter divergence or low filtering precision. The improved Sage-Husa estimator is used to estimate the st...

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Bibliographic Details
Main Authors: Han Chunyao, Xiong Jiajun, Zhang Kai
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:MATEC Web of Conferences
Subjects:
adaptive unscented Kalman filter;
time-varying process noise statistic estimator;
arget tracking;
motion model;
Online Access:https://doi.org/10.1051/matecconf/201713900186

Internet

https://doi.org/10.1051/matecconf/201713900186

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