2019/07/23 by Simone Carlo Surace, Surace, Simone Carlo, Anna Kutschireiter +3
Computer Science · Mathematics · #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Probability (math.PR) #Statistical Methods and Bayesian Inference #Target Tracking and Data Fusion in Sensor Networks #math.OC #math.PR
paper · pdf · doi:10.48550/arxiv.1907.10143
6 pages, 3 figures
openalex publication_date 2019/07/23 · arxiv created 2019/10/30 · arxiv updated 2019/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The filtering of a Markov diffusion process on a manifold from counting process observations leads to `large' changes in the conditional distribution upon an observed event, corresponding to a multiplication of the density by the intensity function of the observation process. If that distribution is represented by unweighted samples or particles, they need to be jointly transformed such that they sample from the modified distribution. In previous work, this transformation has been approximated by a translation of all the particles by a common vector. However, such an operation is ill-defined on a manifold, and on a vector space, a constant gain can lead to a wrong estimate of the uncertainty over the hidden state. Here, taking inspiration from the feedback particle filter (FPF), we derive an asymptotically exact filter (called ppFPF) for point process observations, whose particles evolve according to intrinsic (i.e. parametrization-invariant) dynamics that are composed of the dynamics of the hidden state plus additional control terms. While not sharing the gain-times-error structure of the FPF, the optimal control terms are expressed as solutions to partial differential equations analogous to the weighted Poisson equation for the gain of the FPF. The proposed filter can therefore make use of existing approximation algorithms for solutions of weighted Poisson equations.