A proposal function determines the random particle support of a particle filter. When this support is distributed close to the true target density, filter's estimation performance increases for a given number of particles. In this paper, a proposal strategy for joint state-space tracking using particle filters is given. The state-spaces are assumed Markovian and not-exact; however, each state-space is assumed to sufficiently describe the underlying phenomenon. The joint tracking is achieved by carefully placing the random support of the joint filter to where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space through the proposed strategy.