Abstract
For some safety-critical applications, the risk indicator is represented as a time series of estimation errors in the case of a linear model. The safety of the system is compromised if the probability that this risk indicator leaves a given confidence zone at least once during a certain period becomes too important. Sometimes, we are also interested in the calculation of the instantaneous risk probability. The main difficulty is that the Cumulative Distribution Functions (CDFs) (with infinite support) of measurement noise in the above-mentioned linear model are unknown and only their upper and lower bounds are available. The present paper continues the study of previously developed conservative bounds for the above-mentioned risk probabilities as functions of the bounds for the measurement noise CDFs. The original contribution of the present paper consists in the generalization of the previously obtained results to the case of a linear model.
The author gratefully acknowledges the partial support of this work from the Thales Alenia Space, France.
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Nikiforov, I. (2018). Risk Overbounding for a Linear Model. In: Vishnevskiy, V., Kozyrev, D. (eds) Distributed Computer and Communication Networks. DCCN 2018. Communications in Computer and Information Science, vol 919. Springer, Cham. https://doi.org/10.1007/978-3-319-99447-5_14
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DOI: https://doi.org/10.1007/978-3-319-99447-5_14
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