Name
Title | ||
|---|---|---|
Machine Learning Techniques for Coherent CFAR Detection Based on Statistical Modeling of UHF Passive Ground Clutter. |
Abstract | ||
|---|---|---|
Ultra high frequency (UHF) passive ground clutter statistical models were determined from real data acquired by a passive radar for the design of approximations to the Neyman-Pearson detector based on machine learning techniques. The cross-ambiguity function was the input space without any preprocessing. The Gaussian model was proved to be suitable for high Doppler values. Other models were propos... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTSP.2017.2780798 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Clutter,Passive radar,Detectors,Doppler effect,Surveillance,Radar clutter | Computer vision,False alarm,Computer science,Clutter,Bistatic radar,Artificial intelligence,Statistical model,Constant false alarm rate,Doppler effect,Detector,Passive radar,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 1 | 1932-4553 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nerea del-Rey-Maestre | 1 | 17 | 6.18 |
| Pilar Jarabo-Amores | 2 | 58 | 7.26 |
| David de la Mata-Moya | 3 | 52 | 12.99 |
| Jose-Luis Barcena-Humanes | 4 | 18 | 5.90 |
| Pedro Gomez-del-Hoyo | 5 | 3 | 2.44 |