In strong sea clutter conditions, floating target echo signals are easily overwhelmed. Conventional mode decomposition and reconstruction methods struggle to reliably identify and select the modes that actually contain the target components. This paper proposes a spectral mode reconstruction method based on an adaptive selection criterion for target frequency intervals. Target modes are identified by combining the Doppler frequency shift of the target and the statistical spectral characteristics of sea clutter. An evaluation framework centered on relative feature gain and feature detection probability is then developed to validate the effectiveness of the proposed method. Experimental results on measured data demonstrate that the reconstructed signals significantly outperform the original signals on every metric evaluated, effectively suppressing sea clutter and enhancing target components.

Under strong sea clutter conditions, radar echoes from moving sea-surface targets are easily masked by background clutter. A major limitation of existing mode decomposition and reconstruction approaches is the difficulty in reliably distinguishing target-related modes. To address this challenge, a radar target feature detection method based on Correlational Mode Reconstruction is proposed. The original radar signal is first decomposed into a set of intrinsic mode functions using Variational Mode Decomposition. An ideal target reference model is then constructed by combining time–frequency analysis with morphological image processing. A multi-dimensional correlation metric is then developed to evaluate the degree of match between each mode and the model signal, enabling accurate selection of target modes. Features are subsequently extracted from the reconstructed signal in multiple transform domains, and a relative feature gain metric is used to select discriminative features. The experimental results demonstrate that, irrespective of the feature-based detector used, the proposed reconstruction provides better detection performance than the original signals and the other reconstruction strategies. In addition, feature fusion further improves detection performance over single feature detection.