Neural network detectors for composite hypothesis tests

Authors:
D. de la Mata-Moya;P. Jarabo-Amores;R. Vicen-Bueno;M. Rosa-Zurera;F. López-Ferreras
Affiliations:
Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 6
Cited 3

Performance Analysis of Neural Network Detectors by Importance Sampling Techniques

Neural Processing Letters
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
Review:

Neural Computation
MLP and RBFN for detecting white gaussian signals in white gaussian interference

IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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

Study of two error functions to approximate the Neyman-pearson detector using supervised learning machines

IEEE Transactions on Signal Processing
Sea clutter neural network classifier: feature selection and MLP design

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
SONN and MLP based solutions for detecting fluctuating targets with unknown doppler shift in gaussian interference

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Neural networks (NNs) are proposed for approximating the Average Likelihood Ratio (ALR). The detection of gaussian targets with gaussian autocorrelation function and unknown one-lag correlation coefficient, ρs, in Additive White Gaussian Noise (AWGN) is considered. After proving the low robustness of the likelihood ratio (LR) detector with respect to ρs, the ALR detector assuming a uniform distribution of this parameter in [0,1] has been studied. Due to the complexity of the involved integral, two NN based solutions are proposed. Firstly, single Multi-Layer Perceptrons (MLPs) are trained with target patterns with ρs varying in [0,1]. This scheme outperforms the LR detector designed for a fixed value of ρs. MLP with 17 hidden neurons is proposed as a solution. Then, two MLPs trained with target patterns with ρs varying in [0,0.5] and [0.5,1], respectively, are combined. This scheme outperforms the single MLP and allows to determine a solution of compromise between complexity and approximation error. A detector composed of MLPs with 17 and 8 hidden units each one is proposed.