Time-frequency analysis as a tool for improving neural detectors for low probability of false alarm

Author

Amores, Pilar Jarabo ; Zurera, Manuel Rosa ; Ferreras, Francisco López ; Manso, Manuel Utrilla

Author_Institution

Departamento de Teoria de la Senal y Comunicaciones, Univ. de Alcala, Madrid, Spain

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

91

Abstract

This paper deals with the application of time-frequency analysis for transforming the received radar echoes in order to facilitate a neural network classification task. So as to compress the time-frequency representations maintaining most of the information, a feature extractor is designed. The proposed detector is compared with a single Multilayer Perceptron (MLP). The results show that time-frequency decompositions improve the performance of neural networks for slow fluctuating radar targets detection, specially for low values of Probability of False Alarm. The performance of the new detector is nearly independent on the Training-Signal-to-Noise-Ratio (TSNR) and the training initial conditions

Keywords

feature extraction; neural nets; pattern classification; probability; radar computing; radar detection; radar signal processing; time-frequency analysis; detection scheme; feature extractor design; low false alarm probability; neural network classification task; received radar echoes; slow fluctuating radar target detection; time-frequency analysis; time-frequency representations compression; Detectors; Feature extraction; Multilayer perceptrons; Neural networks; Radar cross section; Radar detection; Radar scattering; Rayleigh scattering; Time frequency analysis; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 2001. ICECS 2001. The 8th IEEE International Conference on

Print_ISBN

0-7803-7057-0

Type

conf

DOI

10.1109/ICECS.2001.957680

Filename

957680