文件名称:OnusingLikelihood-adjustedProposalsinParticleFilte
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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.
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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