Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21020438 | SENSORS |
Keywords | DocType | Volume |
particle filter, auxiliary, adaptive, extended Kalman, tutorial | Journal | 21 |
Issue | ISSN | Citations |
2 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jos Elfring | 1 | 0 | 0.68 |
| Elena Torta | 2 | 0 | 0.34 |
| René van de Molengraft | 3 | 194 | 23.48 |