Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973755 | Digital Signal Processing | 2017 | 13 Pages |
Abstract
In financial applications, it is common practice to fit return series by AutoRegressive Moving-Average (ARMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. In this paper, we develop a complex-valued ARMA-GARCH model for the sea clutter modeling application. Compared with the AR-GARCH model, the additionally introduced MA terms make the proposed model capable of considering the dependence of conditional variances of adjacent echo measurements as model coefficients, improving the modeling precision by taking advantage of the strong correlations between adjacent measurements. Based on the complex-valued ARMA-GARCH process for sea clutter modeling, we further develop a sea surface target detection algorithm. By analyzing a large number of the practical sea clutter data, we evaluate its performance and show that the proposed sea surface target detector offers a noticeable improvement for the probability of detection, comparing with the state-of-the-art AR-GARCH detector.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Yunjian Zhang, Yixiong Zhang, Zhenmiao Deng, Xiao-Ping Zhang, Hui Liu,