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Correlation Beam Scanning for Real Aperture Forward-Looking Imaging Using Beta-Bernoulli Bayesian Learning Algorithm
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DOI：10.1016/j.dsp.2026.106032.png)
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Abstract
En 中文
In modern radar systems, beam scanning and high-resolution imaging are crucial for enhancing target identification accuracy and improving anti-interference capabilities. However, traditional techniques face limitations in beam control and outlier handling. This paper proposes a correlation-based beam scanning method using a hybrid excitation array, which achieves efficient beam scanning by transmitting randomly modulated wideband signals and applying a secondary weighting. The minimum condition number of the secondary modulated radiation field matrix is adopted as the optimization objective, and the simulated annealing (SA) algorithm is employed to jointly optimize the transmitted signals and reference angle to enhance the spatiotemporal incoherence. For real aperture forward-looking imaging, the Beta-Bernoulli prior variational sparse Bayesian learning (BP-VSBL) algorithm is adopted for imaging to identify and handle outliers. Variational Bayesian inference (VBI) is used to estimate hidden variables, and a grid pruning strategy is introduced to reduce computational complexity. Simulation results demonstrate that the proposed method achieves approximately 2 dB first sidelobe suppression while maintaining comparable mainlobe width and peak gain to conventional phased arrays. Moreover, it preserves favorable imaging performance under outlier contamination, thereby validating its robustness and effectiveness.
Keywords:
beam scanning
real aperture imaging
correlation-based method
Beta-Bernoulli prior
sparse Bayesian learning
Journal
D
IF:
3
Papers:
653
Citations:
0
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