TY - GEN
T1 - Discrete State Estimation with Persistent Sensor Faults and Non-Persistent Noise via Noisy Bayesian Active Diagnosis
AU - Yong, Sze
AU - Ozay, Necmiye
N1 - Funding Information:
This work was supported in part by an Early Career Faculty grant from NASA’s Space Technology Research Grants Program and DARPA grant N66001-14-1-4045.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - In this paper, we consider adaptive decision-making problems for stochastic discrete state estimation with a given budget of sensing actions/measurements that yield noisy and/or faulty partial observations. This problem is an extension of Bayesian active diagnosis, which is known to be NP-hard, to the setting when the sensor measurements are vector-valued and may be affected by persistent sensor faults and/or non-persistent noise. In particular, we identify meaningful reward functions for this problem that are adaptive monotone and weakly adaptive submodular; thus an adaptive greedy algorithm (with no need for proxy reward functions nor new algorithms) has guaranteed near-optimal performance. Finally, we apply our approach to discrete state estimation via active sensing of an electrical power system with sensor faults (persistent noise) and sensor noise (stochastic/non-persistent noise).
AB - In this paper, we consider adaptive decision-making problems for stochastic discrete state estimation with a given budget of sensing actions/measurements that yield noisy and/or faulty partial observations. This problem is an extension of Bayesian active diagnosis, which is known to be NP-hard, to the setting when the sensor measurements are vector-valued and may be affected by persistent sensor faults and/or non-persistent noise. In particular, we identify meaningful reward functions for this problem that are adaptive monotone and weakly adaptive submodular; thus an adaptive greedy algorithm (with no need for proxy reward functions nor new algorithms) has guaranteed near-optimal performance. Finally, we apply our approach to discrete state estimation via active sensing of an electrical power system with sensor faults (persistent noise) and sensor noise (stochastic/non-persistent noise).
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U2 - 10.23919/ACC.2018.8431639
DO - 10.23919/ACC.2018.8431639
M3 - Conference contribution
AN - SCOPUS:85052573514
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 313
EP - 320
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 -