High-Resolution ISAR Imaging with Modified Joint Range Spatial-Variant Autofocus and Azimuth Scaling

Abstract

Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.

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

“…In practice, there are mainly two modes of motion errors in the echo signal received by the radar, namely coherent mode (CM) and non-coherent mode (NCM). The former is caused by the steady motion of the target, and the latter is caused by the severe vibration of the target or the range tracking error [18]. Ideally, when the range alignment is completed, the aligned HRRP can be expressed as:…”

“…Typical range alignment methods are mainly sorted into two categories. One is based on the correlation of adjacent pulses, including maximum cross-correlation method [17] and cumulative cross-correlation method [18]. The other is global optimization methods, including global minimum entropy (GME) method [19], global maximum contrast (GMC) method [20] and Gram matrix method [21].…”

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network

“…With the increasing complexity of electromagnetic environment, electronic warfare plays an important role in modern warfare [ 1 ]. Inverse synthetic aperture radar (ISAR) is widely used in modern radar warning systems (RWS), which provides high-resolution situation-awareness information for combat [ 2 , 3 , 4 , 5 , 6 , 7 ]. Noteworthy, the widely developed micro-motion feature extraction technology [ 8 , 9 , 10 , 11 , 12 ] makes it possible to realize a better target identification that is immune to the traditional ISAR jamming methods.…”