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Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle...
This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines...
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