A particle-based nonlinear filtering scheme will be presented. This algorithm is based on implicit sampling, a new sampling technique related to chainless Monte Carlo. Posterior densities are represented by pseudo-Gaussians and the filter is designed to focus particle paths sharply so as to reduce the number of particles needed in the nonlinear data assimilation. Examples will be given.