认知雷达作为一种新概念雷达,还有一些关键技术亟待解决,杂波/干扰的抑制技术就是其中之一。真实环境下杂波/干扰一般是非均匀的,机载认知雷达为了达到抑制杂波和干扰的目的,通常需要事先选择均匀样本并剔除干扰样本。样本质量的好坏直接影响到机载认知雷达杂波和干扰抑制性能的好坏。为此,文中设计了一种基于数字高程模型数据的样本选择方法。新方法首先从数字高程模型数据中提取雷达照射区域内的多种地形因子;接着,根据地形因子选择均匀样本;最后,采用知识辅助的非均匀检测器剔除含干扰的样本。仿真结果表明:文中设计的方法可以很好地完成对均匀样本的挑选以及对干扰目标的剔除,明显优于现有相关方法,可以有效地提高机载认知雷达的杂波和干扰抑制能力。

Cognitive radar is a kind of new concept radar, but there are still so many key technical problems to be solved, one of which is the suppression of clutter and interference. In real environment, clutters and interference are generally non-homogenous. In order to suppress the clutter and interference in airborne cognitive radar, the selection of homogenous samples and elimination of interference samples should be implemented in the first time. The quality of the selected samples directly affects the performance of airborne cognitive radar. Therefore, this paper designed a sample selection method based on digital elevation model (DEM) data. Firstly, the multiple terrain factors were extracted from DEM data. Then, the homogenous samples were selected based on terrain factors. Finally, the knowledge-aided non-homogenous detector is used to eliminate the samples with the interference. The simulation results show that the proposed method performs well in selecting homogenous samples and eliminating interference samples, and is obviously better than the existing related methods. It can effectively improve the capacity of airborne cognitive radar to suppress clutter and interference.

TN959.73

航空科学基金资助项目