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- W2912412583 abstract "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." @default.
- W2912412583 created "2019-02-21" @default.
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- W2912412583 date "2018-12-01" @default.
- W2912412583 modified "2023-10-16" @default.
- W2912412583 title "Optimal Sensor Placement for Kalman Filtering in Stochastically Forced Consensus Networks" @default.
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- W2912412583 doi "https://doi.org/10.1109/cdc.2018.8619288" @default.
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