ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar (ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation (FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity (RV) estimation of the slow rotation target. In this paper, we propose an accurate cross-range scaling algorithm based on the multiple signal classification (MUSIC) method. We first select some range bins with the mono-component linear frequency modulated (LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.