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使用自回归滑动平均(ARMA)和广义自回归条件异方差(GARCH)过程对金融数据建模是经济学常用手段。文中结合ARMA过程和GARCH过程的非线性化扩展模型,将其扩展到复数域,适合于海杂波建模应用。相比传统的海杂波模型及原始的GARCH模型,文中提出的模型在概率密度函数拟合上具有明显的优势。此外,新模型还可准确地捕获相邻海杂波中存在的强相关性。实际雷达海杂波数据验证了该模型的准确性和有效性。
Modeling financial data using autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedastic (GARCH) processes is a common economic approach. In this paper, the non-linear expansion model of ARMA process and GARCH process is extended to the complex number domain, which is suitable for the application of sea clutter modeling. Compared with the traditional sea clutter model and the original GARCH model, the proposed model has obvious advantages in fitting the probability density function. In addition, the new model can accurately capture the strong correlations existing in adjacent sea clutter. The actual radar sea clutter data verify the accuracy and validity of the model.