复杂杂波环境中机动扩展目标检测理论与方法

Target detection in the complex clutter environment is one of the research hotspots in the radar detection and signal processing field. The complex clutter has the characters of time's nonstationary, space's inhomogeneous and distribution's non-Gaussian feature. And the target has the complex scattering struction, complex manoeuvring movement and complex micro-motion character, which causes the target's range-spread and Doppler-spread. Here, we need to resolve the spread target detection problem in the nonstationary, inhomogeneous and non-Gaussian clutter. In details, we research the following problems:(1)the research of a new multi-rank signal subspace model for modeling the radar target returns more precisely;(2)the estimation of the covariance matrix of the cells under test in the inhomogeneous clutter based on the knowledge and Bayesian framework;(3)the design of the modified adaptive normalized match filter, which is related with the tail character of the amplitude probability density function of the clutter;(4)the characters seletction and integration method design of the target. Our group has the abundant experience of the correlative research field. The research problems are innovatory and practicable, which has wide application foreground and scientific sence.

复杂杂波环境下的目标检测是当今国际雷达检测、雷达信号处理领域研究的热点问题之一。杂波的复杂性主要表现为杂波的时间非平稳性、空间非均匀性和幅度分布的非高斯性。目标的复杂性表现为目标的散射结构复杂、运动方式复杂和微动特征复杂，这导致了目标在多普勒和距离维的扩展。本项目将着重解决非平稳非均匀非高斯杂波背景下机动扩展目标的检测问题，重点解决：(1)精确建立目标回波的多秩信号子空间模型；(2)基于知识和贝叶斯方法的复杂杂波体制下的待检测单元的协方差矩阵估计；(3)与杂波幅度概率密度函数拖尾特征相关的修正自适应归一化匹配滤波器的设计；(4)目标特征的选择和有效的特征积累方法的设计。本项目课题组已有丰富的相关研究积累，拟研究问题创新性强，具有很强的实用性，既有重要的科学意义和广泛的应用前景。

复杂地海杂波背景下的目标检测是现代雷达信号处理的一个重要研究方向。复杂地海杂波背景下的复杂目标检测技术研究对于我国远程国土资源保卫、领海保卫、海上护航和海上搜救等都有着重要的意义。杂波的复杂性主要表现为时间非平稳性、空间非均匀性和幅度分布非高斯性。目标的复杂性表现为目标的散射结构复杂、运动方式复杂和微动特征复杂，这导致了目标在多普勒和距离维的扩展。本项目着重解决非平稳非均匀非高斯杂波背景下扩展目标的检测问题。在不同的杂波背景下，我们提出了对应的距离扩展目标检测方法，形成了一系列的研究成果。本项目提出的方法有效地积累了扩展目标多脉冲回波，并发展了多种非平稳非均匀杂波背景下的杂波协方差矩阵估计新方法，提出了与杂波幅度分布匹配的最优检测方法并提出了多种基于特征的目标检测方法。这些重要的研究成果已经成功应用于多个实验雷达或实际雷达系统中，对于强噪声和杂波背景下的弱目标检测性能的提升提供了理论支持和算法支撑。