基于奇异值分解的风电场杂波微动特征提取

Micro-motion features extraction of wind farm echoes based on singular value decomposition

风电场作为一种特殊的大型建筑物,会影响航管监视雷达对飞机目标的准确探测。同时,由于风电场通常分布在某一大面积区域,风轮机之间具有多径传输特性,进而会影响航管监视雷达对飞机目标的定位和测速。因而,分析风电场杂波的微动特征有助于检测和识别风电场杂波信号,提高雷达探测目标的准确性。基于航管监视雷达风电场回波信号模型,利用奇异值分解技术分析了风电场回波信号的微动特征,并构造相应的特征量实现其微动特征的提取。同时,在飞机目标存在背景下,提取了风电场回波的微动特征,并将其与飞机目标的多普勒特征进行对比分析,仿真结果证明了所提方法的有效性。

As a special kind of large buildings, wind farm will affect the accurate detection of the aircraft target by the air traffic control radar. Simultaneously, wind farms are usually composed of many wind turbines distributed in a large area and possess the characteristics of multipath transmission, so they can cause the traffic control radar to misjudge the location and radial speed of the targets. The analysis of the micro-motion features of wind farms echoes is of great significance for the identification and detection of the wind farms clutter accurately. Based on the model of wind farm echoes for air traffic control radar, the micro-motion feature of wind farm echoes signal is analyzed by using the singular value decomposition technique, and then the micro-motion feature of wind farm echoes signal is also extracted by constructing the corresponding characteristic variables. Simuhaneously, the micro-motion feature of the wind farm echoes is analyzed in the presence of the aircraft target and compared with that of the aircraft target echoes. The simulation results demonstrate the effectiveness of the method proposed in this paper.