Name
Abstract | ||
|---|---|---|
One of the major problems in multiple sensor surveillance systems is inadequate sensor registration. We propose a new approach to sensor registration based on layered neural networks. The nonparametric nature of this approach enables many different kinds of sensor biases to be solved. As part of the implementation we develop some modifications to the common network training algorithm to tackle the inherent randomness in all components of the training set. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/7.826314 | IEEE Trans. Aerospace and Electronic Systems |
Keywords | Field | DocType |
Neural networks,Target tracking,Sensor systems,Surveillance,Radar tracking,Sensor phenomena and characterization,Particle measurements,Noise measurement,Trajectory,State estimation | Radar tracker,Noise measurement,Soft sensor,Visual sensor network,Supervised learning,Sensor fusion,Artificial intelligence,Artificial neural network,Machine learning,Mathematics,Randomness | Journal |
Volume | Issue | ISSN |
36 | 1 | 0018-9251 |
Citations | PageRank | References |
9 | 0.94 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| HAIM KARNIELY | 1 | 9 | 0.94 |
| Hava T. Siegelmann | 2 | 980 | 145.09 |