Meter-wave radar height estimation based on sparse Bayesian learning under refined reflection model

Derui Tang

doi:10.1016/j.sigpro.2025.110267

Meter-wave radar height estimation based on sparse Bayesian learning under refined reflection model

2025-09-01

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DOI：10.1016/j.sigpro.2025.110267

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• This paper develops a refined multipath model that incorporates modulation effects from reflective surface, better matching complex real-world terrains than classical point-based approaches. • Combined with a sparse Bayesian off-grid framework and hyperparameter optimization, the method reduces model mismatch and off-grid error, enhancing low-elevation target height estimation. • Simulations and measured data validate the method’s superiority, especially in complex coastal areas, demonstrating its effectiveness and refined model’s performance.

Meter-wave radar height estimation based on sparse Bayesian learning under refined reflection model

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