An Improved Parametric Translational Motion Compensation Algorithm for Targets With Complex Motion Under Low Signal-to-Noise Ratios

• Published in: IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
• Main Authors: Ding, Zegang, Zhang, Guangwei, Zhang, Tianyi, Gao, Yongpeng, Zhu, Kaiwen, Li, Linghao, Wei, Yi
• Format: Article
• Language: English
• Subjects: Algorithms; Compensation; Computer simulation; Doppler effect; Doppler sonar; Entropy; Imaging; Inverse synthetic aperture radar; Inverse synthetic aperture radar (ISAR); Linear phase; low signal-to-noise ratio (SNR); Marine vehicles; Mathematical models; Motion compensation; Movement; Parameters; Particle swarm optimization; Polynomials; Radar; Radar imaging; SAR (radar); signal phase order reduction (SPOR); Signal to noise ratio; Translation; translation motion compensation; Translational motion; Unmanned aerial vehicles
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3217030

Translational motion compensation plays an important role in inverse synthetic aperture radar (ISAR) imaging. However, existing translational motion compensation algorithms cannot work well when the signal-to-noise ratio (SNR) is low and the target has complex motion at the same time, as the algorithms usually assume that these two situations do not occur simultaneously. To address this problem, an improved parametric translational motion compensation algorithm based on signal phase order reduction (SPOR) and minimum entropy is proposed. The key is to decrease the phase order of the signal, which has a nonlinear phase and corresponds to the complex motion, and then obtain the signal with a linear phase corresponding to the noncomplex motion. Subsequently, the signal is transformed into the Doppler domain to generate the SPOR result. It is obvious that, when the translational motion is well compensated, the SPOR result will be coherently accumulated and has the best quality, which means that the SPOR result has good robustness against the low SNR. Thus, the translational motion is modeled as a polynomial model, the entropy of the SPOR result is taken as the optimizing target, and the relationship between the translational motion compensation parameters and the entropy is established. Finally, coarse search and particle swarm optimization (PSO) are sequentially performed to optimize the entropy of the SPOR result and estimate the translational motion compensation parameters accurately and efficiently. Computer simulation results and experimental results based on unmanned aerial vehicle (UAV) radar validate the proposed algorithm.