One key assumption of Belief Space Planning (BSP) is that the data association is known perfectly. In this paper, we relax this assumption in the context of non-myopic planning as well as belief being a Gaussian Mixture Model (GMM). Interestingly, explicit reasoning about the data association within the belief enables our framework to have parsimonious data association, thereby resulting in a scalable solution compared with naïve permutational approaches. Unlike in some of the recent approaches where the number of components in a GMM belief can only be reduced, in our approach this can also go up such as due to perceptual aliasing present in the environment. Furthermore, our approach naturally integrates with inference, providing a unified framework for robust passive and active perception. We demonstrate key aspects of our approach and its comparison with the state of the art on a general abstract domain as well as in a real robot setup.