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Title: Optimal Sensor Placement for Kalman Filtering in Stochastically Forced Consensus Networks
Given a linear dynamical system affected by noise, we consider the problem of optimally placing sensors (at design-time) subject to certain budget constraints to minimize the trace of the steady-state error covariance of the Kalman filter. Previous work has shown that this problem is NP-hard in general. In this paper, we impose additional structure by considering systems whose dynamics are given by a stochastic matrix corresponding to an underlying consensus network. In the case when there is a single input at one of the nodes in a tree network, we provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. However, we show that when the network has multiple inputs, the optimal sensor placement problem becomes NP-hard.  more » « less
Award ID(s):
1635014
NSF-PAR ID:
10086131
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2018 IEEE Conference on Decision and Control (CDC)
Page Range / eLocation ID:
6686 to 6691
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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