TY - GEN
T1 - Projection-based linear constrained estimation and fusion over long-haul links
AU - Liu, Qiang
AU - Rao, Nageswara S.V.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - We study estimation and fusion with linear dynamics in long-haul sensor networks, wherein a number of sensors are remotely deployed over a large geographical area for performing tasks such as target tracking, and a remote fusion center serves to combine the information provided by these sensors in order to improve the overall tracking accuracy. In reality, the motion of a dynamic target might be subject to certain constraints, for instance, those defined by a road network. We explore the accuracy performance of projection-based constrained estimation and fusion methods that is affected by information loss over the long-haul links. We use an example to compare the tracking errors under various implementations of centralized and distributed projection-based estimation and fusion methods and demonstrate the effectiveness of using projection-based methods in these settings.
AB - We study estimation and fusion with linear dynamics in long-haul sensor networks, wherein a number of sensors are remotely deployed over a large geographical area for performing tasks such as target tracking, and a remote fusion center serves to combine the information provided by these sensors in order to improve the overall tracking accuracy. In reality, the motion of a dynamic target might be subject to certain constraints, for instance, those defined by a road network. We explore the accuracy performance of projection-based constrained estimation and fusion methods that is affected by information loss over the long-haul links. We use an example to compare the tracking errors under various implementations of centralized and distributed projection-based estimation and fusion methods and demonstrate the effectiveness of using projection-based methods in these settings.
KW - Long-haul sensor networks
KW - error covariance matrices
KW - linear constraints
KW - projection method
KW - root-mean-square-error (RMSE) performance
KW - state fusion
UR - http://www.scopus.com/inward/record.url?scp=85015167331&partnerID=8YFLogxK
U2 - 10.1109/MFI.2016.7849527
DO - 10.1109/MFI.2016.7849527
M3 - Conference contribution
AN - SCOPUS:85015167331
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
SP - 436
EP - 441
BT - 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
Y2 - 19 September 2016 through 21 September 2016
ER -
