Kolia Sadeghi : August 4

again

I will attempt to take the latest paper on the deterministic particle flow filter discussed in a previous blog post, and strip it down to the essentials.  The authors present a more general, stable and improved version of their previous deterministic particle flow filter, supposedly. This paper is rife with ideas and peculiarly written; for a gentler introduction, please refer to the papers linked to in the previous blog post. Here is the paper:

Exact particle flow for nonlinear filters by Fred Daum, Jim Huang and Arjang Noushin,
Numerical experiments for nonlinear filters with exact particle flow induced by log-homotopy (companion paper)

Deterministic particle filtering

No resampling, rejection, or importance sampling are used. Particles are propagated through time by numerically integrating an ODE. The method is very similar in spirit to Jascha Sohl-Dickstein, Peter Battaglino and Mike DeWeese's Minimum probability flow learning, but applied to nonlinear filtering.


The authors report orders of magnitude speedups for higher dimensional state spaces where sampling rejection would be a problem.

Particle flow for nonlinear filters with log-homotopy by Fred Daum & Jim Huang

There are a couple of papers companion to this one:
Nonlinear filters with particle flow induced by log-homotopy

Seventeen dubious methods to approximate the gradient for nonlinear filters with particle flow


As you may see, the authors have a very peculiar writing style.


However, one very recent paper by Lingji Chen and Raman Mehra points out some flaws in the approach:
A study of nonlinear filters with particle flow induced by log-homotopy
(but see the group meeting announcement above for Fred Daum and Jim Huang's recent answer to this).