Signal estimation in clutter using SVM-based chaos synchronization

Authors:
Di He
Affiliations:
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 8
Cited 0

Motion from Three Weak Perspective Images Using Image Rotation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
On a Pattern-Oriented Model for Intrusion Detection

IEEE Transactions on Knowledge and Data Engineering
A nonlinear filter for estimating a sinusoidal signal and itsparameters in white noise: on the case of a single sinusoid

IEEE Transactions on Signal Processing
Narrowband weak signal detection by higher order spectrum

IEEE Transactions on Signal Processing
A general efficient method for chaotic signal estimation

IEEE Transactions on Signal Processing
Sinusoidal signals with random amplitude: least-squares estimatorsand their statistical analysis

IEEE Transactions on Signal Processing
Methods for chaotic signal estimation

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel approach for estimating signal parameter in clutter using chaos synchronization based on support vector machine (SVM) is proposed. Assuming that the clutter process is chaotic, chaos synchronization is found to be able to extract the weak signal even when the signal is totally embedded inside the clutter spectrum. When the dynamics of the chaotic system is unknown, an SVM-based chaos synchronization is proposed here to estimate the signal parameters. The unbiasedness of the proposed approach is evaluated theoretically. Computer simulations on estimating sinusoidal frequencies confirm that the weak target frequencies can be estimated accurately. Using mean square error (MSE) as the performance measure, the proposed method is shown to have a better performance than the conventional frequency estimation techniques including the fast Fourier transform (FFT) and multiple signal classification (MUSIC) algorithm.