Deep FPF: Gain function approximation in high-dimensional setting

TL;DR

This paper introduces a deep learning-based method to approximate the gain function in high-dimensional feedback particle filters, enabling scalable and effective solutions for complex probabilistic estimation problems.

Contribution

It proposes a neural network-based approach to approximate the gain function, leveraging variational formulation and stochastic optimization for high-dimensional settings.

Findings

Effective in high-dimensional problems

Scales well with number of particles

Outperforms existing methods in experiments

Abstract

In this paper, we present a novel approach to approximate the gain function of the feedback particle filter (FPF). The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The numerical problem is to approximate the exact gain function using only finitely many particles sampled from the probability distribution. Inspired by the recent success of the deep learning methods, we represent the gain function as a gradient of the output of a neural network. Thereupon considering a certain variational formulation of the Poisson equation, an optimization problem is posed for learning the weights of the neural network. A stochastic gradient algorithm is described for this purpose. The proposed approach has two significant properties/advantages: (i) The stochastic optimization algorithm allows one to process, in parallel, only a batch of samples (particles) ensuring good scaling properties with the number of particles; (ii) The remarkable representation power of neural networks means that the algorithm is potentially applicable and useful to solve high-dimensional problems. We numerically establish these two properties and provide extensive comparison to the existing approaches.