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基于复杂非线性系统相空间重构理论, 提出了一种混沌背景中微弱信号检测的选择性支持向量机集成的方法, 为了提高支持向量机集成的泛化能力, 采用K均值聚类算法选择每簇中精度最高的子支持向量机进行集成, 建立了混沌背景噪声的一步预测模型, 从预测误差中检测湮没在混沌背景噪声中的微弱目标信号(包括周期信号和瞬态信号), 最后分别以Lorenz系统和实测的IPIX雷达数据作为混沌背景噪声进行实验研究, 结果表明该方法能够有效地将混沌背景噪声中极其微弱的信号检测出来, 抑制噪声对混沌背景信号的影响, 与神经网络和传统支持向量机方法相比, 预测精度和检测门限方面的性能有显著提高.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.
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Keywords:
- support vector machine/
- ensemble/
- clutter/
- weak signal estimation
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