100 research outputs found
Multi-sensor Suboptimal Fusion Student's Filter
A multi-sensor fusion Student's filter is proposed for time-series
recursive estimation in the presence of heavy-tailed process and measurement
noises. Driven from an information-theoretic optimization, the approach extends
the single sensor Student's Kalman filter based on the suboptimal
arithmetic average (AA) fusion approach. To ensure computationally efficient,
closed-form density recursion, reasonable approximation has been used in
both local-sensor filtering and inter-sensor fusion calculation. The overall
framework accommodates any Gaussian-oriented fusion approach such as the
covariance intersection (CI). Simulation demonstrates the effectiveness of the
proposed multi-sensor AA fusion-based filter in dealing with outliers as
compared with the classic Gaussian estimator, and the advantage of the AA
fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure
Distributed Joint Attack Detection and Secure State Estimation
The joint task of detecting attacks and securely monitoring the state of a cyber-physical system is addressed over a cluster-based network wherein multiple fusion nodes collect data from sensors and cooperate in a neighborwise fashion in order to accomplish the task. The attack detection–state estimation problem is formulated in the context of random set theory by representing joint information on the attack presence/absence, on the system state, and on the attack signal in terms of a hybrid Bernoulli random set (HBRS) density. Then, combining previous results on HBRS recursive Bayesian filtering with novel results on Kullback–Leibler averaging of HBRSs, a novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network
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