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

Area of Science:

• Radar Imaging
• Signal Processing
• Target Recognition

Background:

• High-resolution inverse synthetic aperture radar (ISAR) images are crucial for feature extraction and target recognition.
• Target motion errors, particularly the shift of the equivalent rotational center (ERC), degrade ISAR image quality by disrupting azimuth scaling.
• Existing motion compensation methods may fail to fully address these spatial-variant motion errors.

Purpose of the Study:

• To propose a novel high-resolution ISAR imaging algorithm, the modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS).
• To address the failure of traditional azimuth scaling due to ERC shift after motion compensation.
• To simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus for improved image focusing.

Main Methods:

• Establishment of a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model.
• Application of the Davidon-Fletcher-Powell (DFP) algorithm to solve a minimum entropy optimization problem.
• Joint estimation of ERCP and ERV to enable simultaneous azimuth scaling and range spatial-variant autofocus.

Main Results:

• The MJAAS algorithm effectively performs joint estimation of ERCP and ERV.
• Simultaneous accurate azimuth scaling and range spatial-variant autofocus are achieved, significantly improving image focusing performance.
• The algorithm demonstrates robustness across different motion error modes (coherent/non-coherent) and motion compensation techniques.

Conclusions:

• The proposed MJAAS algorithm offers a practical and accurate solution for high-resolution ISAR imaging.
• MJAAS overcomes limitations of conventional methods in handling spatial-variant motion errors.
• Experimental validation on simulated and real data confirms the algorithm's effectiveness and wide applicability.