Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Comparison of a neural network detector vs Neyman-Pearson optimal detector
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Approximating the Neyman-Pearson detector for swerling I targets with low complexity neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Neural networks for signal detection in non-Gaussian noise
IEEE Transactions on Signal Processing
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In this paper, a Multilayer Perceptron (MLP) is proposed as a radar detector of known targets in Weibull-distributed clutter. The MLP is trained in a supervised way using the Levenberg-Marquardt back-propagation algorithm to minimize the Mean Square Error, which is able to approximate the Neyman-Pearson detector. Due to the impossibility to find analytical expressions of the optimum detector for this kind of clutter, a suboptimum detector is taken as reference, the Target Sequence Known A Priori (TSKAP) detector. Several sizes of MLP are considered, where even MLPs with very low sizes are able to outperform the TSKAP detector. On the other hand, a sensitivity study with respect to target parameters, as its doppler frequency, is made for different clutter conditions. This study reveals that both detectors work better for high values of target doppler frequency and one-lag correlation coefficient of the clutter. But the most important conclusion is that, for all the cases of the study, the MLP-based detector outperforms the TSKAP one. Moreover, the performance improvement achieved by the MLP-based detector is higher for lower probabilities of false alarm than for higher ones.