Signal Estimation in Clutter Using SVM-Based Chaos Synchronization

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.

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