In Particle Filtering and Smoothing

Smoothed Particle Filtering for Dynamic Bayesian Networks

Particle filtering (PF) for dynamic Bayesian networks (DBNs) with discrete-state spaces includes a resampling step which concentrates samples according to their relative weight in regions of interest of the state-space. We propose a more systematic approach than resampling based on regularisation (smoothing) of the empirical distribution associated with the samples, using the kernel method. We ...

A Comparative Performance Analysis of Filtering and Smoothing Techniques on a Simulated Unmanned Aircraft System

In order for unmanned aircraft systems (UAS) to fly safely into civil airspace, the development of vigorous unmanned aircraft’s Accident Avoidance System (UAS-AAS) is rather crucial. However, accurate localization is a key prerequisite for a UAS-AAS to be successful. This paper investigate the application of two filtering and smoothing techniques to a UAS given the aircraft dynamics, sensor per...

Forgetting in Marginalized Particle Filtering and its Relation to Forward Smoothing, Report no. LiTH-ISY-R-3009

The problem of degeneracy in marginalized particle filtering is addressed. In particular, we note that the degeneracy is caused by loss of entropy of the posterior distribution and design maximum entropy estimates to prevent this. The main technique used in this report is known as forgetting. It is shown that it can be used to suppress the problem with degeneracy, however, it is not a proper cu...

Sequential Monte Carlo Methods for Filtering and Smoothing in Hidden Markov Models

Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models

Filtering and smoothing algorithms that estimate the integrated variance in Lévy-driven stochastic volatility models are analyzed. Particle filters are algorithms designed for nonlinear, nonGaussian models while the Kalman filter remains the best linear predictor if the model is linear but non-Gaussian. Monte Carlo experiments are performed to compare these algorithms across different specifica...