超分辨距离—多普勒成像研究

介绍几种超分辨成像方法,包括线性预测数据外推的离散傅立叶变换(LPDEDFT)法、动态优化线性最小二乘(DOLLS)法和Hopfjeld神经网络非线性最小二乘(HNNNLS)法,并给出B-52飞机缩比金属模型微波暗室转台实测数据和Boeing-727飞机外场实测数据的成像结果。比较了这些方法在分辨能力、计算复杂性等方面的优劣。采用这些超分辨成像方法,与普通的傅立叶方法相比,在相同信号带宽和总转角的条件下可以获得更高的图像分辨率,或用较小的信号带宽和总转角可以获得相同质量的图像。

STUDIES OF SUPERRESOLUTION RANGE-DOPPLER IMAGING

This paper presents three superresolution imaging methods, including the linear prediction data extrapolation DFT (LPDEDFT), the dynamic optimization linear least squares (DOLLS), and the Hopfield neural network nonlinear least squares (HNNNLS). The live data of a metalized scale model B-52 aircraft mounted on a rotating platform in a microwave anechoic chamber and a flying Boeing-727 aircraft were processed. The imaging results indicate that, compared to the conventional Fourier method, either higher resolution for the same effective bandwidth of transmitted signals and total rotation angle in imaging or equal-quahty images from smaller bandwidth and total rotation angle may be obtained by these superresolution approaches. Moreover, these methods are compared in respect of their resolution capability and computational complexity.