Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Design of a Pre-processing Stage for Avoiding the Dependence on TSNR of a Neural Radar Detector
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
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
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This paper deals with the application of Multilayer Perceptrons to radar detection. The dependence of the neural detector performance on the network size and on the signal-to-noise ratio selected for training is considered. Multilayer Perceptrons with different numbers of neurons in the hidden layer have been trained using different values of the signal-to-noise ratio to minimize the mean square error using the error back-propagation algorithm. Results show that the higher the number of hidden neurons, the closer the neural detector to the Neyman-Pearson optimum detector and the lower the dependence of the Multilayer Perceptron performance on the signal-to-noise ratio selected for training. Due to its practical interest, the very low probability of false alarm values has been considered. To estimate the probability of a false alarm, importance sampling techniques have been used in order to reduce the computational cost of maintaining a low relative error.