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CLC number: TP274+.2

On-line Access: 2016-05-04

Received: 2015-08-17

Revision Accepted: 2016-02-16

Crosschecked: 2016-04-11

Cited: 2

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Citations:  Bibtex RefMan EndNote GB/T7714


Xie Wang


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.5 P.449-457


A novel approach of noise statistics estimate using H filter in target tracking

Author(s):  Xie Wang, Mei-qin Liu, Zhen Fan, Sen-lin Zhang

Affiliation(s):  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   wangxiek@zju.edu.cn, liumeiqin@zju.edu.cn, fanzhen@zju.edu.cn, slzhang@zju.edu.cn

Key Words:  Noise estimate, H∞, filter, Target tracking

Xie Wang, Mei-qin Liu, Zhen Fan, Sen-lin Zhang. A novel approach of noise statistics estimate using H filter in target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5): 449-457.

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author="Xie Wang, Mei-qin Liu, Zhen Fan, Sen-lin Zhang",
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T1 - A novel approach of noise statistics estimate using H filter in target tracking
A1 - Xie Wang
A1 - Mei-qin Liu
A1 - Zhen Fan
A1 - Sen-lin Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500262

Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H; filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H; filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

This paper deals with the noise statistics estimation problem in target tracking. By introducing the H filter instead of other conventional filters, more accurate noise samples could be obtained, which would lead to more exact estimates of noise mean and covariance. Overall, this paper is interesting and of some significance.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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