Joint Translational Motion Compensation of ISAR Imaging for Uniformly Accelerated Motion Targets Based on MPD-TSLS Under Low SNR

Effective and accurate translational motion compensation is crucial for inverse synthetic aperture radar (ISAR) imaging. Traditional data-based translational motion compensation methods are inapplicable to uniformly accelerated targets in low signal-to-noise ratio (SNR) scenarios. In this paper, a parametric approach is proposed based on joint modified phase difference and two-step least squares (MPD-TSLS). The method employs a second-order polynomial model for translational motion, whereby the energy of all scatterers is converted to a single range and Doppler cell through MPD operation, respectively. The estimated acceleration and velocity are then obtained by TSLS. To enhance precision, an optimization approach based on LS for refining polynomial parameters is also employed. The proposed method achieves a substantial increase in SNR, ensuring precise compensation accuracy while maintaining high computational efficiency by relying exclusively on fast Fourier transform (FFT) and matrix operations. The experimental results obtained from both simulated and real datasets fully verify that the proposed method exhibits superior performance compared with the other implemented methods.