Kurtosis-based estimation of cross-range scaling factor for high-resolution inverse synthetic aperture radar imaging

In automatic target recognition systems based on the use of inverse synthetic aperture radar (ISAR) images, it is essential to obtain unbiased and accurate scaled two-dimensional target images in the range-cross range domain. To accomplish this, the modulus of the target effective rotation vector, which is generally unknown for noncooperative targets, must be estimated. This letter proposes an efficient method for estimating the cross-range scaling factor and significantly improving cross-range resolution based on the second-order local polynomial Fourier transform. The estimation requires solving a series of one-dimensional optimizations of a kurtosis objective. Simulations show the proposed approach to be effective and able to accurately estimate the scaling factor in the presence of noise.