Name
Title | ||
|---|---|---|
Robustness with respect to the signal-to-noise ratio of MLP-based detectors in Weibull clutter |
Abstract | ||
|---|---|---|
The Neyman-Pearson detector can be approximated by MultiLayer Perceptrons (MLPs) trained in a supervised way to minimize the Mean Square Error. The detection of a known target in a Weibull-distributed clutter and white Gaussian noise is considered. Because of the difficulty to obtain analytical expressions for the optimum detector under this environment, a suboptimum detector like the Target Sequence Known A Priori (TSKAP) detector is taken as reference. A study of the MLP size shows as a low complexity MLP-based detector trained with the Levenberg-Marquardt algorithm to minimize the MSE is able to obtain good performances. Low performance improvement is achieved for greater sizes than 20 hidden neurons. The MLP-based detector is better than the TSKAP one, even for very low complexity MLPs (6 inputs, 5 hidden neurons and 1 output). Moreover, it is demonstrated empirically that both detectors are robust with respect to changes in the target parameters (signal to noise ratio). So, MLP-based detectors are proposed to detect known targets in Weibull-distributed clutter plus white Gaussian noise. |
Year | Venue | Field |
|---|---|---|
2007 | European Signal Processing Conference | Pattern recognition,Computer science,Clutter,Signal-to-noise ratio,Mean squared error,Robustness (computer science),Artificial intelligence,Constant false alarm rate,Perceptron,Additive white Gaussian noise,Detector |
DocType | ISBN | Citations |
Conference | 978-839-2134-04-6 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| raul vicenbueno | 1 | 55 | 7.55 |
| M. Pilar Jarabo-Amores | 2 | 19 | 7.17 |
| Manuel Rosa-Zurera | 3 | 192 | 36.27 |
| David de la Mata-Moya | 4 | 52 | 12.99 |
| roberto gilpita | 5 | 57 | 7.79 |