Particle Filters: A Hands-On Tutorial

Abstract

The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and code examples. Extensive research has advanced the standard particle filter algorithm to improve its performance and applicability in various ways in the years after. As a result, selecting and implementing an advanced version of the particle filter that goes beyond the standard algorithm and fits a specific estimation problem requires either a thorough understanding or reviewing large amounts of the literature. The latter can be heavily time consuming especially for those with limited hands-on experience. Lack of implementation details in theory-oriented papers complicates this task even further. The goal of this tutorial is facilitating the reader to familiarize themselves with the key concepts of advanced particle filter algorithms and to select and implement the right particle filter for the estimation problem at hand. It acts as a single entry point that provides a theoretical overview of the filter, its assumptions and solutions for various challenges encountered when applying particle filters. Besides that, it includes a running example that demonstrates and implements many of the challenges and solutions.

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

“…An alternative approach for real-time state estimation via recursive Bayesian estimation uses Monte-Carlo based filters. This approach accommodates the sequential particle filter (PF) and its variants, which are not limited to the assumption of Gaussian noise [25], [26]. All of these MB estimators require accurate knowledge of the underlying SS model, and their performance is typically degraded in the presence of model mismatch.…”

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

“…The estimation of unknown states involves a batch filter, and that of the known states involves recursive filters. This study focuses on recursive filters such as the Kalman filter (KF), EKF, UKF, and particle filter (PF) [29][30][31][32]. Recursive filters can be further categorized into two types: linear and non-linear filters.…”

Interaction-Aware Intention Estimation at Roundabouts

“…In the update step, a measurement Z t is used to refine the expected state estimate. In the resampling step, new particles are randomly selected, with replacement, from the set of weighted particles [31]. Each particle represents a possible state of the situation and contains a specific corridor to each vehicle.…”