TY - JOUR
T1 - Distributed extended object tracking information filter over sensor networks
AU - Li, Zhifei
AU - Liang, Yan
AU - Xu, Linfeng
AU - Ma, Shuli
N1 - Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2023/1/25
Y1 - 2023/1/25
N2 - This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.
AB - This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.
KW - distributed consensus estimate
KW - extended object tracking
KW - nonlinear control system
KW - sequential processing
KW - wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85140099588&partnerID=8YFLogxK
U2 - 10.1002/rnc.6425
DO - 10.1002/rnc.6425
M3 - 文章
AN - SCOPUS:85140099588
SN - 1049-8923
VL - 33
SP - 1122
EP - 1149
JO - International Journal of Robust and Nonlinear Control
JF - International Journal of Robust and Nonlinear Control
IS - 2
ER -