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On using likelihood-adjusted proposals in particle filtering: local importance sampling
2005
Image and Signal Processing and Analysis
An unsatisfactory property of particle filters is that they may become inefficient when the observation noise is low. In this paper we consider a simple-to-implement particle filter, called 'LIS-based particle filter', whose aim is to overcome the above mentioned weakness. LIS-based particle filters sample the particles in a two-stage process that uses information of the most recent observation, too. Experiments with the standard bearings-only tracking problem indicate that the proposed new
doi:10.1109/ispa.2005.195384
fatcat:3clb7wu4mzavfd5d5poz6qwgpq