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Multi-Channel Coupled Variational Bayesian Framework with Structured Sparse Priors for High-Resolution Imaging of Complex Maneuvering Targets

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Udgivet i:Remote Sensing vol. 17, no. 14 (2025), p. 2430-2460
Hovedforfatter: Wang, Xin
Andre forfattere: Yang, Jing, Luo, Yong
Udgivet:
MDPI AG
Fag:
Sparsity
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
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Resumen: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.
ISSN:2072-4292
DOI:10.3390/rs17142430
Fuente:Advanced Technologies & Aerospace Database