Name
Abstract | ||
|---|---|---|
This letter presents a novel multi-sensor probability hypothesis density (PHD) filter for multi-target tracking by means of multiple or even massive sensors that are linked by a fusion center or by a peer-to-peer network. As a challenge, we find there is little known about the statistical properties of the sensors in terms of their measurement noise, clutter, target detection probability, and even... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LCOMM.2018.2863387 | IEEE Communications Letters |
Keywords | Field | DocType |
Sensor fusion,Noise measurement,Clutter,Position measurement,Probability,Peer-to-peer computing | Data mining,Probability hypothesis density filter,Noise measurement,Computer science,Clutter,Filter (signal processing),Sensor fusion,Real-time computing,Fusion center,Missing data,Cluster analysis | Journal |
Volume | Issue | ISSN |
22 | 10 | 1089-7798 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiancheng Li | 1 | 96 | 8.40 |
| Javier Prieto | 2 | 6 | 1.49 |
| Hongqi Fan | 3 | 32 | 11.25 |
| Juan M. Corchado | 4 | 2899 | 239.10 |