Name
Abstract | ||
|---|---|---|
In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The proposed tracking strategy is based on a Bayesian framework, in which the dynamic target state is augmented to include the sensor detection probabilities. The method is validated using computer simulations and real-world experiments conducted by the NATO Science and Technology Organization (STO) - Centre for Maritime Research and Experimentation (CMRE). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICASSP.2015.7178428 | IEEE International Conference on Acoustics, Speech and SP |
Keywords | Field | DocType |
Multiple sensors, real-world data, Bayesian target tracking, particle filter, time-varying performance | Data mining,Computer science,Clutter,Signal-to-noise ratio,Atmospheric measurements,Artificial neural network,Wireless sensor network,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.36 |
References | Authors | |
17 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Papa | 1 | 11 | 1.20 |
| Paolo Braca | 2 | 467 | 46.44 |
| Steven Horn | 3 | 12 | 2.21 |
| Stefano Maranò | 4 | 371 | 40.52 |
| Vincenzo Matta | 5 | 338 | 40.78 |
| Peter Willett | 6 | 1962 | 224.14 |