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On using Likelihood-adjusted Proposals in Particle Filtering: Local Importance Sampling

, and . 4th International Symposium on Image and Signal Processing and Analysis, page 1--2. (2005)

Abstract

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 particle filter method is indeed a viable alternative to other methods.

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