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.
Highlights
Inverse synthetic aperture radar (ISAR) can image non-cooperative targets and obtain high-resolution two-dimension (2-D) images, serving as an effective tool for radar target recognition with broad applications in civilian and military fields [1,2,3,4,5,6]
The three-dimension (3-D) ISAR imaging geometry model is shown in Figure 1a, where the radar line of sight (RLOS) direction from the radar to the rotational center of the target O is defined as the Y-axis
This explains why it is of vital importance to propose an algorithm which can simultaneously compensate the range spatial-variant phase error and overcome the distortion of azimuth scaling caused by the equivalent rotational center position (ERCP) deviation
Summary
Inverse synthetic aperture radar (ISAR) can image non-cooperative targets and obtain high-resolution two-dimension (2-D) images, serving as an effective tool for radar target recognition with broad applications in civilian and military fields [1,2,3,4,5,6]. The effective rotational velocity (ERV) of the target can be obtained by the estimated linear coefficient, and the azimuth scaling can be achieved This kind of method has been widely applied because of its small computation and high accuracy. It is desired to advance an algorithm by jointly estimating the equivalent rotational center position (ERCP) and effective rotational velocity (ERV), which can achieve the range spatial-variant autofocus while precisely accomplishing azimuth scaling. (1) By analyzing the processing results of the motion compensation algorithm, a novel joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established It takes into account the deviation of the ERC caused by the motion compensation algorithm, and jointly estimates the ERCP and ERV, so as to achieve the modification of the ERCP. MJAAS can jointly achieve accurate azimuth scaling and range spatial-variant autofocus by estimating the corresponding ERCP and ERV with regard to different motion compensation algorithms.
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