TY - JOUR
T1 - Adaptive filtering for stochastic systems with generalized disturbance inputs
AU - Liang, Yan
AU - Zhou, Donghua
AU - Zhang, Lei
AU - Pan, Quan
PY - 2008
Y1 - 2008
N2 - This letter presents a new class of discrete-time linear stochastic systems with the statistically-constrained disturbance input, which can represent an arbitrary linear combination of dynamic, random, and deterministic disturbance inputs to generalize the complicated modeling error encountered in actual applications. An adaptive filtering scheme is proposed for such systems by recursively constructing and adaptively minimizing the upper-bounds of covariance matrices of the state predictions, innovations, and estimates. The minimum-upper-bound filter is then obtained via online scalar convex optimization. The experiment on maneuvering target tracking shows that the proposed filter can significantly reduce the peak estimation errors due to maneuvers, compared with the well-known IMM method.
AB - This letter presents a new class of discrete-time linear stochastic systems with the statistically-constrained disturbance input, which can represent an arbitrary linear combination of dynamic, random, and deterministic disturbance inputs to generalize the complicated modeling error encountered in actual applications. An adaptive filtering scheme is proposed for such systems by recursively constructing and adaptively minimizing the upper-bounds of covariance matrices of the state predictions, innovations, and estimates. The minimum-upper-bound filter is then obtained via online scalar convex optimization. The experiment on maneuvering target tracking shows that the proposed filter can significantly reduce the peak estimation errors due to maneuvers, compared with the well-known IMM method.
KW - Adaptive Kalman filtering
KW - Discrete time systems
KW - Stochastic systems
UR - http://www.scopus.com/inward/record.url?scp=67650120956&partnerID=8YFLogxK
U2 - 10.1109/LSP.2008.2002707
DO - 10.1109/LSP.2008.2002707
M3 - 文章
AN - SCOPUS:67650120956
SN - 1070-9908
VL - 15
SP - 645
EP - 648
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
ER -