Radar Signal Sorting for Rotating Interferometer Based on Hough Transform-RANSAC

As a core component of radar emitter reconnaissance technology, the performance of radar signal sorting directly affects the accuracy of subsequent emitter threat level assessment, battlefield situation analysis and decision-making. At present, the performance of clustering-based radar signal sorting degrades significantly when the parameter overlap of Pulse Description Word (PDW) is severe. To address this issue, this paper proposes a radar signal sorting algorithm combining the Hough Transform and RANSAC, which leverages the phase difference characteristics of rotating interferometers. First, a clustering algorithm is used for presorting radar pulse signals to dilute high-density pulses and reduce the computational load of subsequent main sorting. Second, the Hough Transform is employed to detect and group linear features in binary images of phase differences, while the Random Sample Consensus (RANSAC) algorithm is used for detecting and grouping parabolic features. Finally, the main sorting is completed by calculating the forward difference slopes of data points corresponding to linear and parabolic features. The simulation results demonstrate that, in the presence of severe overlap among radar signal PDW parameters, the proposed method effectively addresses the issue of missed grouping and significantly improves sorting accuracy compared to existing approaches. Furthermore, even under conditions of severe pulse loss, the proposed method still exhibits strong robustness and reliability.