Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes Using Mixture Density Particle Filters

Mixture density particle filters (MDPFs) can effectively learn discriminative models of state dynamics and observation likelihoods, enabling robust long-range tracking of multimodal posterior distributions without relying on manually engineered generative models.