TY - GEN
T1 - Sensor selection with correlated measurements for target tracking in wireless sensor networks
AU - Liu, Sijia
AU - Masazade, Engin
AU - Fardad, Makan
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/4
Y1 - 2015/8/4
N2 - We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alternative conditional posterior Cramér-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.
AB - We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alternative conditional posterior Cramér-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.
KW - Target tracking
KW - convex optimization
KW - sensor selection
KW - wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84946098301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946098301&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178728
DO - 10.1109/ICASSP.2015.7178728
M3 - Conference contribution
AN - SCOPUS:84946098301
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4030
EP - 4034
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Y2 - 19 April 2014 through 24 April 2014
ER -