Optimally Robust Kalman Filtering at Work: AO-, IO-, and Simultaneously IO- and AO- Robust Filters - Statistics > ComputationReport as inadecuate




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Abstract: We take up optimality results for robust Kalman filtering fromRuckdeschel2001,2010 where robustness is understood in a distributionalsense, i.e.; we enlarge the distribution assumptions made in the ideal model bysuitable neighborhoods, allowing for outliers which in our context may besystem-endogenous-propagating or -exogenous-non-propagating, inducing thesomewhat conflicting goals of tracking and attenuation. Correspondingly, thecited references provide optimally-robust procedures to deal with each type ofoutliers separately, but in case of IO-robustness does not say much about theimplementation. We discuss this in more detail in this paper. Most importantly,we define a hybrid filter combining AO- and IO-optimal ones, which is able totreat both types of outliers simultaneously, albeit with a certain delay. Wecheck our filters at a reference state space model, and compare the resultswith those obtained by the ACM filter Martin and Masreliez1977, Martin1979and non-parametric, repeated-median based filters Fried et al.2006,2007.



Author: Peter Ruckdeschel

Source: https://arxiv.org/







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