Resampling algorithms and architectures for distributed particle filters

By Miodrag Bolic, Petar M. Djuric, Sangjin Hong. IEEE Transactions on Signal Processing, 2005

354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f95dbf53b12e4302073ed86feb35371c1e0ecc88

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. Tiancheng Li
2. Miodrag Bolic
3. Ming Liu
4. Max Qing-Hu Meng
5. Yuxiang Sun
6. Petar M. Djuric
7. Lawrence M. Murray
8. Fredrik K. Gustafsson

In Control and Systems Engineering

1. Ming Liu
2. Max Qing-Hu Meng
3. Tiancheng Li
4. Yuxiang Sun
5. Petar M. Djuric
6. Miodrag Bolic
7. Pierre Del Moral
8. Fredrik K. Gustafsson

In Ocean Engineering

1. Ming Liu
2. Max Qing-Hu Meng
3. Tiancheng Li
4. Yuxiang Sun
5. Petar M. Djuric
6. Miodrag Bolic

Citations in the corpus

• Improving RGB-D SLAM in dynamic environments: A motion removal approach (2017, methodology): “In the re-sampling stage of the particle filter, we use the Sequential Importance Re-sampling (SIR) strategy [37] to generate new particles.”
• Resampling Methods for Particle Filtering: Classification, implementation, and strategies (2015, methodology): “Researchers mainly rely on examples to monitor the change of the mean square error [32], [34] or the effective sample size [33], [40] of the parallel versus sequential implementation of PF.”
• Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013, background): “In the meantime, the particle filter is best suited to shared-memory architectures where the collective operation can be performed efficiently, although approaches for distributed-memory resampling have been proposed [7].”