Compensation of Phase Errors for Compressed Sensing Based Isar Imagery Using Inadequate Pulses

Abstract

Abstract-Due to the inaccuracies in radar's measurement, autofocus including range alignment and phase compensation is always essential in inverse synthetic aperture radar (ISAR) imagery. Compressed sensing (CS) based ISAR imagery suggests that the image of target can be reconstructed from much fewer random pulses. Because the number of pulses is inadequate and the pulse intervals are nonuniform, conventional phase compensating algorithms can't work in CS imaging. In this paper, an iterative algorithm is proposed to compensate the phase errors and reconstruct high-resolution focused image from limited pulses. In each iteration, the image of target is reconstructed by CS method, and then the estimation of phase errors is updated based on the reconstructed image. By cycling these steps, well-focused image can be obtained. The smoothed 0 algorithm is used to reconstruct the image, and the idea of minimum entropy optimization is used to estimate the phase errors. Besides, a method of extracting range bins in range profile based on amplitude information is proposed, which can reduce the computational complexity and improve the speed of convergence considerably. Both simulation and experiment results from real radar data demonstrate the effectiveness and feasibility of our method.

Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

“…where ∂ |z mn | 2 ∂ϕ l = 2Re z * mn • ∂z mn ∂ϕ l , Re {•} represents the real part. Convert Z = H E H S into the matrix element operation to obtain z mn = L l=1 φ * lm s ln exp (−jϕ l ), in which φ lm is the element of the l-th row and m-th column in , s ln is the element of the l-th row and n-th column in S. Then, the first partial derivative of z mn with respect to phase error ϕ l is ∂z mn ∂ϕ l = −jφ * lm s ln exp (−jϕ l ), and substituting it into (27), we can obtain:…”

“…In order to reduce the influence of sparse aperture on phase adjustment, joint imaging and phase error correction technology in the autofocus problem of synthetic aperture radar (SAR) is proposed in [26] for the first time, which provides a solution to the problem of model error compensation in the sparse reconstruction. In [27], fast minimum entropy phase compensation (FMEPC) method is combined with smoothed l 0 norm (SL0) algorithm to realize the joint processing of phase adjustment and ISAR imaging, but it is sensitive to noise. In [28], an alternating direction iterative-shrinkage thresholding algorithm (ADI-STA) is proposed to realize the phase compensation and image autofocusing for randomized stepped frequency ISAR, but does not consider the influence of sparse aperture on imaging quality.…”

Multi-target simultaneous ISAR imaging based on compressed sensing

“…References [22] and [23] suggest the method that minimizes entropy of average range profile, and the eigenvector method can be utilized to solve the range alignment and phase correction of random pulses. Reference [24] proposed a method to solve the problem of motion compensation when pulses are inconsecutive. Combining Eq.…”