TY - GEN
T1 - Sensor Selection via Observability Analysis in Feature Space
AU - Maske, Harshal
AU - Kingravi, Hassan A.
AU - Chowdhary, Girish
N1 - Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - Finding spatial locations of physical sensors is critical to reliably estimating and monitoring spatiotemporal systems, such as weather, traffic, or social networks. Existing sensor placement approaches that leverage mutual information or coverage do not take into account the spatiotemporal dynamics of the underlying phenomena. Leveraging recent work in modeling evolving Gaussian processes, we show that a sensor placement method can be constructed by applying observability theory on linear models of the spatiotemporal phenomena in a higher dimensional feature space. We show that this approach outperforms traditional mutual information based approaches by taking into account the invariant subspaces induced by the spatiotemporal dynamics. Furthermore, fundamental results relating the observability of spatiotemporal phenomena with deterministic and stochastic sensors placement are proven.
AB - Finding spatial locations of physical sensors is critical to reliably estimating and monitoring spatiotemporal systems, such as weather, traffic, or social networks. Existing sensor placement approaches that leverage mutual information or coverage do not take into account the spatiotemporal dynamics of the underlying phenomena. Leveraging recent work in modeling evolving Gaussian processes, we show that a sensor placement method can be constructed by applying observability theory on linear models of the spatiotemporal phenomena in a higher dimensional feature space. We show that this approach outperforms traditional mutual information based approaches by taking into account the invariant subspaces induced by the spatiotemporal dynamics. Furthermore, fundamental results relating the observability of spatiotemporal phenomena with deterministic and stochastic sensors placement are proven.
UR - http://www.scopus.com/inward/record.url?scp=85052564665&partnerID=8YFLogxK
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U2 - 10.23919/ACC.2018.8431625
DO - 10.23919/ACC.2018.8431625
M3 - Conference contribution
AN - SCOPUS:85052564665
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 1058
EP - 1064
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -