GPR hyperbola-preserving clutter suppression method based on curvelet adaptive weighted thresholding

This paper proposes a ground penetrating radar (GPR) clutter suppression method utilizing a hybrid approach combining the F–K transform and an adaptive weighted thresholding strategy in the curvelet domain. Due to the complex underground environment, GPR signals suffer from substantial clutter, including both linear and non-uniform components, which obscures target echoes. Therefore, effective clutter suppression is crucial to enhance the visibility of target signals. While numerous recently developed methods have proven highly effective in suppressing GPR clutter, it remains a challenge to balance clutter suppression with the preservation of target features in complex environments. To further address this, the proposed method adopts a cascade framework: it first utilizes the F–K transform for preliminary linear clutter removal, followed by an adaptive weighted thresholding strategy, which integrates energy distribution and directional information, to precisely suppress residual non-uniform clutter while preserving the integrity of target signatures. The efficacy of the proposed method is validated using both simulated and real data, demonstrating its ability to suppress clutter while maintaining the hyperbolic characteristics of target echoes. Comparative analysis further confirms that the proposed method exhibits superior performance compared to conventional widely employed clutter suppression algorithms.