Analysis of selection methods for cost-reference particle filtering with applications to maneuvering target tracking and dynamic optimization

Abstract

Cost-reference particle filtering (CRPF) is a recently proposed sequential Monte Carlo (SMC) methodology aimed at estimating the state of a discrete-time dynamic random system. The estimation task is carried out through the dynamic optimization of a user-defined cost function which is not necessarily tied to the statistics of the signals in the system. In… (More)
DOI: 10.1016/j.dsp.2006.09.003

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