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Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty

<13> This work opens doors to new solutions to high dimensional and complex problems. Develops a novel methodology, Bernouli Box Particle Filtering, for detection and estimation of nonlinear systems in the presence of triple uncertainty (stochastic, set-theoretic and data association uncertainties), and demonstrates cost efficient implementation of the method. Collaboration with Branko Ristic, DSTO Australia, triggering follow-on research with Fraunhofer Institute, Germany. Published in IEEE TSP, the leading journal in signal processing (IF: 2.628).