CAJ | 학술논문

CAPON算法的谱分辨率与所构造的滤波器长度成反比,但由于涉及样本自相关矩阵估计及其求逆运算,滤波器长度往往必须小于数据长度的一半,极大地限制了谱分辨率的提高.文中通过对实测ISAR回波数据建立AR模型,并利用Burg算法求解AR模型进行数据外推,提高CAPON滤波器的长度上限,再利用CAPON算法进行超分辨ISAR成像.实测数据的仿真表明,与传统的FFT和CAPON算法相比,基于AR和CAPON的联合超分辨算法可以获得更高分辨率的ISAR像.
CAPON산법적보분변솔여소구조적려파기장도성반비,단유우섭급양본자상관구진고계급기구역운산,려파기장도왕왕필수소우수거장도적일반,겁대지한제료보분변솔적제고.문중통과대실측ISAR회파수거건립AR모형,병이용Burg산법구해AR모형진행수거외추,제고CAPON려파기적장도상한,재이용CAPON산법진행초분변ISAR성상.실측수거적방진표명,여전통적FFT화CAPON산법상비,기우AR화CAPON적연합초분변산법가이획득경고분변솔적ISAR상.
The spectral resolution of CAPON algorithm is inversely proportional to the length of the filter. Due to the concern of es-timation of autocorrelation matrix and calculation of matrix inversion, the length of filter is often less than that of data, which great-ly limits the improvement of spectral resolution. In this paper, an AR-model of the ISAR echo data is established and Burg algo-rithm is used during its solution for data extension to increase the upper limit of the length of filter. Then CAPON algorithm is em-ployed for ISAR imaging. Experiment results show that compared with conventional FIT and CAPON method, higher resolution of ISAR imaging can be achieved through super-resolution method based on AR- CAPON.