TY - JOUR
T1 - Linear minimum mean squared estimation of measurement bias driven by structured unknown inputs
AU - Zhou, Lin
AU - Liang, Yan
AU - Zhou, Jie
AU - Yang, Feng
AU - Pan, Quan
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2014.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - In this study, a generalised systematic bias (SB) is presented, which is represented via a dynamic model driven by structured unknown inputs (UI). The online SB estimation is implemented in two steps. In the first step, the state-free SB measurement and the UI-free SB dynamic model are derived in the case that UI-free condition holds. In the second step, the linear minimum mean squared filter is obtained via orthogonal principle, and the sufficient condition of filtering stability is presented. A simulation about target tracking is given to verify the proposed method.
AB - In this study, a generalised systematic bias (SB) is presented, which is represented via a dynamic model driven by structured unknown inputs (UI). The online SB estimation is implemented in two steps. In the first step, the state-free SB measurement and the UI-free SB dynamic model are derived in the case that UI-free condition holds. In the second step, the linear minimum mean squared filter is obtained via orthogonal principle, and the sufficient condition of filtering stability is presented. A simulation about target tracking is given to verify the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84907909634&partnerID=8YFLogxK
U2 - 10.1049/iet-rsn.2013.0311
DO - 10.1049/iet-rsn.2013.0311
M3 - 文章
AN - SCOPUS:84907909634
SN - 1751-8784
VL - 8
SP - 977
EP - 986
JO - IET Radar, Sonar and Navigation
JF - IET Radar, Sonar and Navigation
IS - 8
ER -