摘要
传统的点散射中心模型只能表征目标的位置信息,无法表示目标的长度和角度,而属性散射中心模型表征了目标的几何特性。为了能得到目标的全方位部件信息,鉴于属性散射中心模型参数估计的缘故,提出基于属性散射中心的多视角参数化部件提取与合成算法。首先将大视角有重叠的划分为若干子视角,分别进行属性散射中心模型的参数估计,然后将各参数统一投影到同一坐标系下,再进行参数的融合,最终得到目标参数集。该算法得到的这套参数可以反演目标回波数据,提高图像可视性,进行目标识别与分类。最后用两个仿真实验验证了此算法的有效性。
The traditional point scattering model is no longer valid and the attributed scattering center is put into use when it is necessary to express not only target＇s position, but also the length and the angle. In order to extract all the view of the target＇s components and result for the parameters estimation of the attributed scattering center, a new multi-aspect components extraction and fusion method based on attributed scattering center is proposed. First, the full aspect data is divided into several overlapping sub-aspect to estimate the parameters of every sub-apertures. Then the sub-aperture＇s parameters are projected into the same coordinate system. Finally, all parameters are fused into a parameter set. The proposed method can get a parameter set, which can be used for radar echo inversion, improving the visualization of the radar image, target recognition and classification. Simulation results validate the effectiveness of the novel approach.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2017年第6期1197-1202,共6页
Systems Engineering and Electronics
基金
国家自然科学基金创新群体基金(61621005)
自然科技基金(61301292)资助课题
关键词
参数融合
多视角
属性散射中心模型
参数化部件提取
图像可视性
fuse parameter
multi-aspect
attributed scattering center model
parametric components extraction
visualization of the image
