Title:
OnusingLikelihood-adjustedProposalsinParticleFilte
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Description: 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
With the standard bearings - only tracking problem indicate
The proposed new particle filter method is indeed
Viable alternative to other methods.
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