Author:
Jarabo-Amores María-Pilar,la Mata-Moya David de,Gil-Pita Roberto,Rosa-Zurera Manuel
Abstract
Abstract
The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman–Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Jarabo-Amores M, Rosa-Zurera M, Gil-Pita R, López-Ferreras F: Study of two error functions to approximate the Neyman–Pearson detector using supervised learning machines. IEEE Trans. Signal Proces 2009, 57(11):4175-4181.
2. Neyman J, Pearson K: On the problem of the most efficient test of statistical hypotheses. Philosph. Trans. Roy. Soc. Lond. A 231 1933, 492-510.
3. Trees HV: Detection, estimation, and modulation theory. New York: Wiley; 1968.
4. di Vito A, Naldi M: Robustness of the likelihood ratio detector for moderately fluctuating radar targets. IEE Proc. Radar Sonar Navig 1999, 146(2):107-122. 10.1049/ip-rsn:19990261
5. He Q: MIMO radar diversity with Neyman–Pearson signal detection in non-Gaussian circumstance with non-orthogonal waveforms. In Proceedings of IEEE-ICASSP. Prague, Czech Republic; 2011:2764-2767.
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