A multi-band ISAR high-resolution imaging method, device and equipment

By combining sparse Bayesian learning and nonlinear least squares cost function, the multi-band ISAR imaging method is optimized, solving the problems of cross-band phase mismatch and computational complexity, and generating high-resolution ISAR images.

CN122546212APending Publication Date: 2026-08-11XIDIAN UNIV
0 Cites 0 Cited by

Patent Information

Application Number
CN202610482123.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-13
Publication Date
2026-08-11

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively correct cross-band phase mismatch in multi-band ISAR imaging, while maintaining a balance between signal coherence and computational complexity of control algorithms. Mesh-based methods suffer from high computational complexity, while meshless methods consume enormous amounts of memory and have slow convergence speeds.

Method used

Sparse Bayesian learning is used to perform sparse coarse estimation in the discrete domain. The initial estimate is used to construct a nonlinear least squares cost function, which is then continuously optimized in the neighborhood to update the continuous manifold dictionary and generate a high-resolution ISAR image.

Benefits of technology

This method effectively corrects phase mismatch and maintains signal coherence in multi-band ISAR imaging, while controlling the computational complexity of the algorithm to generate high-resolution ISAR images. It solves the problems of excessive computational complexity of the gridded method and huge memory consumption of the gridless method.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

This invention provides a multi-band ISAR high-resolution imaging method, apparatus, and device. The method includes: performing sparse coarse estimation in the discrete domain using a sparse Bayesian learning method to obtain initial discrete coordinate coarse estimation vectors and initial complex amplitude coarse estimation vectors; constructing a nonlinear least squares cost function using a variable projection method based on a multi-band joint echo theory model and the initial complex amplitude coarse estimation vectors; optimizing the discrete coordinate vectors in the nonlinear least squares cost function within the corresponding neighborhood using the initial discrete coordinate coarse estimation vectors to obtain target coordinate estimation vectors; updating the continuous manifold dictionary in the multi-band joint echo theory model using the target coordinate estimation vectors to calculate the updated complex scattering coefficient vectors; and generating a final high-resolution ISAR image using the target coordinate estimation vectors and the updated complex scattering coefficient vectors. This achieves high-resolution multi-band ISAR imaging.
Need to check novelty before this filing date? Find Prior Art