Weak signal estimation in chaotic clutter using selective support vector machine ensemble

Abstract

A method of detecting weak signals embedded in chaotic noise by selective support vector machine ensemble based on the theory of phase space reconstruction of the complicated nonlinear system is presented. For improving the generalization ability of support vector machine ensemble, K-means algorithm is used to select the most accurate individual support vector machine from every cluster for ensembling It is established a one-step predictive model that detects the weak signal, including transient signal and period is signals, from the predictive error in the chaotic sequences. It is illustrated in the experiment which is conducted to detect weak signals from Lorenz chaotic background and IPIX Sea Clutter, that the proposed method is highly effective to detect weak signal from a chaotic background and to minimize the influence of noise on weak signals, Compared wich the RBF neural network and SVM model, the new method presents great value in predicting accuracy and detection threshold.