Micro-Doppler Effect Removal in ISAR Imaging by Promoting Joint Sparsity in Time-Frequency Domain

Abstract

For micromotion scatterers with small rotating radii, the micro-Doppler (m-D) effect interferes with cross-range compression in inverse synthetic aperture radar (ISAR) imaging and leads to a blurred main body image. In this paper, a novel method is proposed to remove the m-D effect by promoting the joint sparsity in the time-frequency domain. Firstly, to obtain the time-frequency representations of the limited measurements, the short-time Fourier transform (STFT) was modelled by an underdetermined equation. Then, a new objective function was used to measure the joint sparsity of the STFT entries so that the joint sparse recovery problem could be formulated as a constrained minimization problem. Similar to the smoothed l0 (SL0) algorithm, a steepest descend approach was used to minimize the new objective function, where the projection step was tailored to make it suitable for m-D effect removal. Finally, we utilized the recovered STFT entries to obtain the main body echoes, based on which cross-range compression could be realized without m-D interference. After all contaminated range cells were processed by the proposed method, a clear main body image could be achieved. Experiments using both the point-scattering model and electromagnetic (EM) computation validated the performance of the proposed method.

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

“…erefore, combined with its advantages in the high-precision reconstruction for undersampling data [22], the orthogonal matching pursuit (OMP) algorithm is utilized to reconstruct the ISAR image of the main body, but the ability of noise suppression is poor [23]. By improving the joint sparsity of the time-frequency domain, smoothed (SL2L0) algorithm is proposed in [24] to eliminate m-D effects, but the solution to the sparse reconstruction problem is limited by the time-frequency aggregation of STFT. In order to further improve the removal of m-D effects under the conditions of noise and sparse aperture, singular value decomposition [25] and complex variational mode extraction (SVD-CVME) are jointly processed to eliminate the m-D effects and the interference introduced by noise and sparse aperture in the ISAR image of the main body.…”

A RPCA-Based ISAR Imaging Method for Micromotion Targets

“…In [19], a method based on histogram analysis was proposed to remove the Micro-Doppler signal. In [20], the joint sparsity of frequency representation of the main body signal was exploited and a novel method under the sparse representation framework was developed to preserve the components of the main body signal whereas the interference of the micromotion counterparts in time-frequency domain was eliminated. Besides these efforts, another line of works based on the empirical mode decomposition (EMD) and its variants concentrate on the problem of Micro-Doppler separation, but these methods lack theoretical analysis [21][22][23][24].…”