For bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS), it needs to estimate the effective rotational velocity (ERV) and correct linear-geometry distortion at the same time. In this paper, the effective rotational velocity (ERV), rotational center (RC) and ratio of linear-geometry distortion (RLGD) are jointly estimated by optimizing the image quality, which is measured by the image entropy. After parameter estimation and phase compensation, the image without linear-geometry distortion is generated by the matched Fourier transform (MFT). Numerical results validate that the proposed method works robust under different signal to noise ratio (SNR) conditions.
For the high-speed moving target, its high-resolution range profile (HRRP) obtained by wideband radar is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. When the radar echo is under sampled, however, the HRRP will suffer from strong side and grid lobes, which deteriorates the performance of velocity estimation. A novel velocity estimation and compensation of high-speed target for under sampled data is proposed. The variational Bayesian inference based on the Laplacian scale mixture (LSM) prior is utilized to reconstruct HRRP with high resolution from the under sampled data. During the reconstruction of HRRP, the minimum entropy-based Newton method is used to estimate the velocity to compensate the high order phase error. Experimental results validate the effectiveness of the proposed velocity estimation and compensation algorithm.
This paper proposes a Bayesian sparse signal recovery algorithm. To improve performance on sparse representation, the log-Laplacian distribution is first defined. With a narrow main lobe and high tail values, it is used as a prior of the sparse signal to model the sparse characteristic. Note that analytical inference of the posterior of the sparse signal is a challenge, because the proposed log-Laplacian prior is not conjugate to the Gaussian likelihood. A maximum a posterior (MAP) estimation-based sparse signal recovery algorithm is further proposed. During the reconstruction of the sparse signal, MAP and maximum likelihood estimation are utilized to estimate the scaling parameter and noise variance, respectively, so as to avoid manual tuning of parameters. Additionally, with the use of the conjugate-gradient algorithm, large matrix inversion is avoided and computational efficiency is improved. Experimental results based on both simulated and measured data validate the effectiveness of the proposed log-Laplacian prior-based sparse signal recovery algorithm. Further, it is applied to micromotion parameters estimation and inverse synthetic aperture radar imaging to confirm its validity.