A New Parameterized Family of Stochastic Particle Flow Filters

We derive a parameterized family of stochastic particle flows driven by a nonzero diffusion process for nonlinear filtering, Bayesian inference, or target tracking. This new family of stochastic flows takes the form of a linear combination of prior knowledge and measurement likelihood information. It is shown that several particle flows existing in the literature are special cases of this family. We prove that the particle flows are unbiased under the assumption of linear measurement and Gaussian distributions, and examine the consistency of estimates constructed from the stochastic flows. We further establish several finite time stability concepts for this new family of stochastic particle flows.