Resampling Methods for Particle Filtering: Classification, implementation, and strategies

By Tiancheng Li, Miodrag Bolic, Petar M. Djuric. IEEE Signal Processing Magazine, 2015

575 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/289ff331f86827f9b32c8ab3dd65c8c22867b419

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luca Martino
2. Victor Elvira
3. Christian P. Robert
4. Aki Vehtari
5. Andrew Gelman
6. Jonah Gabry
7. Jean-Michel Marin
8. Ingmar Schuster

In Computer Networks and Communications

1. Christian P. Robert
2. Victor Elvira
3. Luca Martino

In Signal Processing

1. Christian P. Robert
2. Victor Elvira
3. Luca Martino

Citations in the corpus

• Rethinking the Effective Sample Size (2018, methodology): “The diversity in the set of samples is also key in IS-based methods that incorporate resampling steps [e.g. particle filtering (Li et al., 2015) or adaptive IS (Bugallo et al., 2017)] to avoid degeneracy, even in the case where the dESS is high.”