A Fast Cross-Range Scaling Algorithm for ISAR Images Based on the 2-D Discrete Wavelet Transform and Pseudopolar Fourier Transform - Li, Dong; Zhang, Chengxiang; Liu, Hongqing; Su, Jia; Tan, Xiaoheng; Liu, Qinghua; Liao, Guisheng (IEEE transactions on geoscience and remote sensing, 2019)
To better interpret the inverse synthetic aperture radar (ISAR) imaging results, it is highly desirable to present them in the homogeneous range-cross-range domain, rather than the conventional range-Doppler (RD) domain. This process is referred to as cross-range scaling and the rotating angle velocity (RAV) of the moving target must be estimated first to achieve that goal. In this paper, an efficient cross-range scaling approach based on 2-D discrete wavelet transform (2D-DWT) and pseudopolar fast Fourier transform (PPFFT) is developed. To be exact, first, 2D-DWT is applied to two sequential ISAR images to obtain the dominant feature points based on the fact that the ISAR images are usually redundant for estimating RAV. By doing so, the data dimensional reduction and noise suppression are also realized. After that, second, via the efficient PPFFT, two sequential RD ISAR images are mapped into the pseudopolar coordinate to convert the rotational motion into the translational motion along the pseudo angle direction. Finally, to estimate the RAV, a new normalized correlation cost function is constructed and the Golden section algorithm is employed to efficiently find the optimal RAV. Compared with the conventional methods, the advantages of the proposed method are threefold: 1) the rotation center of a target is no longer required prior; 2) without the interpolation operation and the utilization of data dimensional reduction via 2D-DWT, the computational complexity of the proposed method is significantly reduced; and 3) the accurate RAV estimation is achieved in the case of low signal-to-noise ratio condition. The results from both the simulated and the measured data demonstrate that the proposed approach outperforms the state-of-the-art algorithms in terms of the estimation accuracy and computational complexity.