基于逆高斯分布的复合高斯海杂波建模研究

基金项目:

国家自然科学基金(61171120)资助课题

Modeling of Compound Gaussian Sea Clutter Based on Inverse Gaussian Distribution

1.
(Institute of Engineering, Beijing Forestry University, Beijing 100083, China)
2.
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)
3.
(Faculty of Science, Beijing Forestry University, Beijing 100083, China)

摘要: 复合高斯(CG)分布模型广泛应用于非高斯杂波建模，其纹理分量决定了杂波的非高斯特性。该文采用逆高斯分布的纹理分量建立了一种双参数复合高斯分布海杂波模型，即逆高斯-复合高斯(IG-CG)分布，并推导了其统计特性。同时，利用IPIX 型雷达杂波数据进行拟合分析，结果表明该文建立的双参数IG-CG 分布模型相对于单参数IG-CG 分布模型和K 分布模型，其残差平方和平均降低了30%和60%，能够更加准确地与实测数据相吻合。
- 海杂波 /
- 复合高斯(CG)分布 /
- K 分布 /
- IG-CG 分布
Abstract: The Compound-Gaussian (CG) distribution is widely used for modeling the non-Gaussian clutter, and its texture component describes the non-Gaussian properties of clutter. In this paper, a CG model with an inverse Gaussian texture distribution is proposed, called Inverse Gaussian Compound Gaussian (IG-CG) distribution, and its distributional properties are derived. The IPIX radar lake-clutter measurements are analyzed, and the results show that the two-parameter IG-CG distribution model fits the real radar data better than single parameter IG-CG distribution model and K distribution model.
- Sea clutter /
- Compound-Gaussian (CG) distribution /
- K distribution /
- IG-CG distribution