Name
Abstract | ||
|---|---|---|
The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo implementation of the PHD filter, known as the particle PHD filter, can give state estimates with nonlinear and non-Gaussian models. Recently, Mahler et al. have introduced a PHD smoother to gain more accurate estimates... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2016.2611138 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Target tracking,Atmospheric measurements,Particle measurements,Signal processing algorithms,Approximation algorithms,Radio frequency,Current measurement | Approximation algorithm,Extended Kalman filter,Control theory,Computer science,Particle filter,Filter (signal processing),Retrodiction,Kernel adaptive filter,Approximation error,Recursion | Journal |
Volume | Issue | ISSN |
23 | 11 | 1070-9908 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengming Feng | 1 | 33 | 4.90 |
| Wenwu Wang | 2 | 333 | 52.60 |
| Syed Mohsen Naqvi | 3 | 417 | 55.49 |
| Jonathon A. Chambers | 4 | 56 | 6.96 |