A radar signal sorting algorithm based on intra-pulse multidimensional feature fusion

In modern electronic warfare, the increasing density and complexity of radar signals reveal critical limitations in traditional inter-pulse parameter-based sorting methods, including batch overlap, pulse leakage, and heightened sensitivity to parameter tolerances. This paper presents a radar signal sorting algorithm leveraging intra-pulse multidimensional feature fusion. We utilize variational mode decomposition to extract signal energy entropy and mode coefficients, apply phase space reconstruction for computing correlation dimension and Lyapunov exponent, and employ intrinsic time-scale decomposition to derive sample entropy with correlation coefficients. These six-dimensional features are fused into a discriminative feature matrix to enhance inter-class separability. An improved density-peak clustering fuzzy C-means algorithm is proposed, which adaptively determines the cluster number and initial centers via density-peak clustering and optimizes membership iteration through fuzzy C-means to address the limitations of traditional clustering algorithms, such as dependency on prior parameters and error accumulation. Hardware-in-the-loop experiments demonstrate that the proposed algorithm outperforms most baseline methods across a wide range of evaluation metrics. It exhibits superior noise robustness under low signal-to-noise ratio (SNR) conditions. It achieves near-optimal performance under high SNR conditions at 5 dB and above, with all metrics exceeding 96 %.