Sea clutter modeling using an autoregressive generalized nonlinear-asymmetric GARCH model

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• 作者：Yunjian ZhangAuthor Vitae ; Zhenmiao Deng ; ¹ ; ^{dzm_ddb@xmu.edu.cn}Author Vitae ; Jianghong ShiAuthor Vitae ; Yixiong ZhangAuthor Vitae ; Hui LiuAuthor Vitae
• 关键词：Radar ; Sea clutter ; GARCH ; Nonlinearity ; Asymmetry
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：62
• 期：Complete
• 页码：52-64
• 全文大小：1040 K
• 卷排序：62

文摘

The sea clutter modeling is critical to the radar design and assessment of relevant detection algorithms. In this paper, we investigate a family of generalized autoregressive conditional heteroscedastic (GARCH) processes to model the sea clutter as a time series, in which the current variance is dependent on historical information. The most general model (so-called the ALLGARCH model) provides more flexible variance structures to model non-Gaussian, asymmetry, and nonlinear properties of the clutter. However, after going through the usage of the ALLGARCH model, we find that it is not very suitable because the coefficients of the model, which are numerous, would be difficult to estimate in a real-time operating environment. Meanwhile, we find that some of the coefficients are negligible under almost all kinds of sea environments and weather conditions. Motivated by these observations, we propose a novel GARCH model for sea clutter modeling, which is a generalization of the nonlinear-asymmetric GARCH (NAGARCH) model. Considering the correlation between adjacent clutter returns, autoregressive terms are also introduced. By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models. Also, we develop the corresponding generalized likelihood ratio test (GLRT) algorithm for the new model. Numerical simulations exhibit that the proposed detector achieves higher probability of detection, comparing with the AR-GARCH detector.