复合高斯海杂波模型下最优相干检测进展  

Development of optimum coherent detection in compound-Gaussian sea clutter models

作　　者：于涵[1] 水鹏朗[1] 施赛楠 杨春娇[1] 

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室,西安710071

出　　处：《科技导报》2017年第20期109-118,共10页Science & Technology Review

摘　　要：不同于噪声背景下的目标检测,增加雷达发射功率对海杂波背景下的目标检测性能并不能带来重大的改善,因此海杂波的精细化建模和海杂波特性的充分利用成为改善目标检测性能的最重要途径。复合高斯模型是目前广泛使用的海杂波模型,为海杂波特性的精细描述提供了有力工具,而相应的最优检测理论和方法为目标检测性能改善提供了技术支持。本文综述复合高斯海杂波模型下最优及近最优相干检测理论和方法。首先,对K分布、广义Pareto分布及逆高斯纹理3种复合高斯模型进行了概述,并介绍了3种模型下已有的最优及近最优检测方法;然后,对目前复合高斯杂波加噪声混合模型下相干检测方法的进展和应用瓶颈进行了分析;最后,针对未来该方面研究的进一步完善,探讨了几种计算可实现近最优检测方法的研究思路。Unlike the radar target detection in a noise background, for the target detection in the background of the sea clutter, the increase of the transmitting power would not bring about a significant performance improvement and thus a refined modeling of the sea clutter and a full exploitation of the characteristics of the sea clutter become very important to improve the performance of the target detection in the background of the sea clutter. The compound-Gaussian model is a widely recognized model to characterize the sea clutter,which provides a powerful tool to implement a refined description of the sea clutter. Moreover, the related optimum detection theory and methods with regard to this model provide the technique for improving the target detection performance in the background of the sea clutter.This paper reviews three compound-Gaussian sea clutter models, including the K distribution, the generalized Pareto distribution and the inverse Gaussian texture and the existing optimum coherent detection and the near-optimum coherent detection with these models. The current development of the optimum coherent detection in the compound-Gaussian clutter plus noise is addressed and their‘bottleneck＇in practical applications is analyzed. At last, we discuss several possible approaches to develop near-optimum and computationally implementable detection methods.

关 键 词：海杂波 复合高斯模型 最优相干检测 近最优相干检测 计算可实现近最优检测方法 

分 类 号：TN957.51[电子电信—信号与信息处理]