Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
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
Neural networks for signal detection in non-Gaussian noise
IEEE Transactions on Signal Processing
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
IEEE Transactions on Signal Processing
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Radar detection of targets in clutter and noise is an usual problem presented in radar systems. Several schemes based on statistical signal processing are proposed as detectors. In some cases, the Neural Networks (NNs) are applied to this problem. In this article, a radar detector based in a class of NN, the MultiLayer Perceptron (MLP), is proposed. This MLP can be trained in a supervised way to minimize the Mean Square Error (MSE) criterion. Moreover, it is demonstrated that the MLP trained in that way approximates the Neyman-Pearson detector. The NN-based detector proposed is compared with a Target Sequence Known A Priori (TSKAP) detector. The last detector is only took as reference because it is not realizable due to it is necessary to know when the target exists and its magnitude and shape. The results show how the proposed detector improves the performance of the TSKAP one for different conditions of the target measured with the Signal-to-Noise Ratio (SNR) and the skewness or shape parameter (a) of the Weibull-distributed clutter. Finally, several figures show which is the improvement of the NN-based detector.