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.