Neural networks for signal detection in non-Gaussian noise

Gandhi, P.P.; Ramamurti, V.

doi:10.1109/78.650111

Cited by 65 publications

(26 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, (21) with (22) is a particular case of (1). Taking (22) into (8), the unconstrained optimal solution of f * x (x) can be expressed as follows…”

Section: Misclassification Error Objective Functionmentioning

confidence: 97%

See 1 more Smart Citation

Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

Vicen-Bueno

Jarabo-Amores

Rosa-Zurera

et al. 2010

Neural Process Lett

View full text Add to dashboard Cite

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training.

show abstract

“…Then, (21) with (22) is a particular case of (1). Taking (22) into (8), the unconstrained optimal solution of f * x (x) can be expressed as follows…”

Section: Misclassification Error Objective Functionmentioning

confidence: 97%

“…Other works [21][22][23], which considered the use of NNs to approximate communication and radar detectors, have highlighted the poor performance of such detectors for low P e and P f a values. NN-based detectors have been compared to the Neyman-Pearson optimum detector in [22].…”

Section: Introductionmentioning

confidence: 98%

Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

Vicen-Bueno

Jarabo-Amores

Rosa-Zurera

et al. 2010

Neural Process Lett

View full text Add to dashboard Cite

show abstract

“…Recently, neural networks have been extensively studied and suggested for applications in many areas of signal processing. Signal detection using neural network is a recent trend [3] - [6]. In [3] Watterson generalizes an optimum multilayer perceptron neural receiver for signal detection.…”

Section: Introductionmentioning

confidence: 99%

“…Gandhi and Ramamurti [6], [7] has shown that the neural detector trained using BP algorithm gives near optimum performance. The performance of the neural detector using BP algorithm is better than the Matched Filter (MF) detector, used for detection of Gaussian and non-Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

A Neural Solution for Signal Detection In Non-Gaussian Noise

Khairnar

Merchant

Desai

2007

Fourth International Conference on Information Technology (ITNG'07)

View full text Add to dashboard Cite

show abstract

“…This scheme has been used previously is several works [1,[18][19][20]. An equivalent implementation consist in varying the bias of the output neuron [21,22]. A different approach is used in [23]: a two-output NN with outputs in (0, 1) was used, comparing the subtraction of both outputs to a threshold.…”

Section: Introductionmentioning

confidence: 99%

Radar detection with the Neyman–Pearson criterion using supervised-learning-machines trained with the cross-entropy error

Jarabo-Amores

Mata-Moya

Gil-Pita

et al. 2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

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.

show abstract

Neural networks for signal detection in non-Gaussian noise

Cited by 65 publications

References 7 publications

Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

A Neural Solution for Signal Detection In Non-Gaussian Noise

Radar detection with the Neyman–Pearson criterion using supervised-learning-machines trained with the cross-entropy error

Contact Info

Product

Resources

About