Improvements in the implementation of log-homotopy based particle flow filters

State estimation of a non-linear system perturbed by non Gaussian noise is a challenging task. Typical solutions like EKF/UKF could fail while Monte Carlo methods, even though more accurate, are computationally expensive. Recently proposed log homotopy based particle flow filter, also known as Daum-Huang filter (DHF) provides an alternative way of non-linear state estimation. There have been a number of DHFs derived, based on solutions of the homotopy flow equation. The performance of these new filters depends a lot on the implementation methodology. In this paper, we highlight the key factors affecting the DHF performance and investigate them individually. We then make recommendations based on our results. It is shown that a properly designed DHF can outperform a basic particle filter, with less execution time.