GO-CFAR Trained Neural Network Target Detectors
| Autor: | Karl Erik Olsen, Jabran Akhtar |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Detector Process (computing) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Statistical power law.invention Constant false alarm rate Noise law 0202 electrical engineering electronic engineering information engineering Clutter 020201 artificial intelligence & image processing Artificial intelligence Radar business |
| Zdroj: | 2019 IEEE Radar Conference (RadarConf). |
| DOI: | 10.1109/radar.2019.8835765 |
| Popis: | Detecting targets embedded in noise and clutter is an essential task for many radar systems. A competent system must additionally offer high probability of detection with a low false alarm rate and a standard practice is to employ constant false alarm rate (CFAR) detectors. In this article, we develop and expand the use of neural networks to accomplish this objective. The neural networks are trained to recognize targets in a specified environment subject to the proposed conditions ascribed by a traditional CFAR detector. We show that after an initial learning process, a trained neural network can offer improved detectional performance. The improvement is related to either a lower false alarm rate or a slightly greater probability of detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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