Name
Abstract | ||
|---|---|---|
In the work presented here we propose a novel bicoherence-based method for the classification of aerial radar targets in automatic target recognition (ATR) systems. The possibility of classifying aerial targets using the micro-Doppler contributions caused by a jet engine or the rotor of a helicopter is studied. The method is based on classification features computed in the form of bicoherence estimates, as well as cepstral coefficients extracted from the micro-Doppler contribution contained in radar returns. The performance of the classification method developed is compared with the performance of common methods using high-resolution radar range profiles (HRRPs). Correct classification probability rates are computed for three different types of aerial targets. The benefits achieved by using bicoherence-based classification features are demonstrated and discussed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TAES.2014.120266 | IEEE Trans. Aerospace and Electronic Systems |
Keywords | Field | DocType |
Radar,Feature extraction,Helicopters,Blades,Training,Hidden Markov models | Radar engineering details,Mel-frequency cepstrum,Electronic engineering,Artificial intelligence,Doppler effect,Radar,Bicoherence,Computer vision,Automatic target recognition,Pattern recognition,Inverse synthetic aperture radar,Rotor (electric),Mathematics | Journal |
Volume | Issue | ISSN |
50 | 2 | 0018-9251 |
Citations | PageRank | References |
8 | 0.59 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pavlo O. Molchanov | 1 | 8 | 0.59 |
| Karen O. Egiazarian | 2 | 16 | 1.25 |
| Jaakko Astola | 3 | 1515 | 230.41 |
| Alexander V. Totsky | 4 | 20 | 2.65 |
| Sergey Leshchenko | 5 | 8 | 0.59 |
| Pilar Jarabo-Amores | 6 | 58 | 7.26 |