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High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details.
Details
Accuracy;
Image resolution;
Receiving;
Optimization;
Signal processing;
Maneuvering targets;
Remote sensing;
Data processing;
Image degradation;
Parameter robustness;
Image processing;
Multichannel communication;
Joining;
Sampling;
Radar imaging;
Robustness;
Inverse synthetic aperture radar;
Probabilistic models;
Bayesian analysis;
Image reconstruction;
Parameter estimation;
Signal to noise ratio;
High resolution;
Convex analysis;
Methods;
Algorithms;
Image quality;
Complexity;
Mathematical models;
Statistical inference
; Luo, Yong 3 1 School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2 School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China, State Key Laboratory of Electromagnetic Space Cognition and Intelligent Control, Beijing 100089, China, Tianfu Jiangxi Laboratory, Chengdu 641419, China
3 School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China, State Key Laboratory of Electromagnetic Space Cognition and Intelligent Control, Beijing 100089, China