机载阵列雷达目标认知检测方法研究

Target detection with airborne radar 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 non-stationary in the time domain, inhomogeneity in space and non-Gaussian feature with probability distribution. Traditional radar target detection algorithms have the problem of insufficient use of the clutter prior knowledge and can't adjust algorithm's parameters as the characteristics of clutter change, which leads to radar target detection degradation. To solve these problems, we focus on the study of clutter knowledge sensing and knowledge-aided target detection within the cognitive radar framework. In details, we research the following problems: 1) the establishment of clutter knowledge set and the analysis and modeling of the clutter characteristics; 2) the design of adaptive detectors with prior knowledge; 3) the selection of adaptive filter algorithms with prior knowledge. These research problems are innovatory and practicable, which have wide application foreground and scientific sence.

面对日益复杂的杂波环境，如何提高机载雷达目标检测性能是雷达信号处理领域的研究热点问题之一。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的非高斯性。传统的机载阵列雷达目标检测算法对杂波先验知识的利用不充分，并且滤波算法策略确定后较少随杂波特性变化而进行调整，导致复杂环境中无法获得满意的目标检测性能。针对这些不足，本项目在认知雷达理论框架下开展杂波先验知识感知和知识辅助的雷达目标认知检测方法研究，主要内容包括：1）建立杂波先验知识库及杂波特性在线分析和建模；2）融合杂波先验知识的自适应检测器设计；3）基于环境先验知识的自适应滤波算法选择方法。项目的研究创新性强，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。

面对日益复杂的杂波环境，如何提高机载雷达目标检测性能是雷达信号处理领域的研究热点问题之一。传统的机载阵列雷达目标检测算法对杂波先验知识的利用不充分，并且滤波算法策略确定后较少随杂波特性变化而进行调整，导致复杂环境中无法获得满意的目标检测性能。针对这些不足，本项目研究知识辅助的雷达目标认知检测理论，以提高复杂环境下机载阵列雷达地面和低空动目标的检测性能为目标。主要研究内容包括：1）环境杂波特性分析与建模；2）知识辅助的样本挑选和空时杂波抑制方法；3）非均匀环境下利用待测样本的稳健STAP方法；4）融合环境杂波知识的雷达自适应检测器设计；5）知识辅助的自适应检测算法选择。所有研究成果的有效性均经过仿真或MCARM实测数据实验验证。研究成果具有重要的科学意义和广泛的应用前景。