Radar Signal Deinterleaving Based on Amplitude Variation Characteristics

Current radar signal deinterleaving methods primarily utilise direction of arrival, radio frequency and pulse repetition interval to separate signals from different radars. However, their effectiveness becomes limited in scenarios with highly overlapping radar parameters. Amplitude can provide supplementary discrimination for many deinterleaving problems, especially for mechanically scanning radars. The amplitude of signals intercepted from such radars exhibits a continuous parabolic‐like variation characteristic. Leveraging this, we construct a function‐adapted Gaussian mixture model to characterise the joint distribution of pulse time‐of‐arrival and amplitude for interleaved pulse trains, thereby transforming radar signal deinterleaving into a parameter estimation and clustering problem. Furthermore, we employ active function cross‐entropy clustering (afCEC) to solve the problem and innovatively embeds the sequential andom sampling consensus within the afCEC framework to mitigate its sensitivity to initial values and avoid local optima. This achieves preliminary clustering of the time‐amplitude data, effectively decomposing the originally pulse point cloud into multiple subclusters conforming to mixture model components. Building upon this over‐segmentation result, we design a merging strategy based on pulse cluster continuity, enabling automatic deinterleaving without prior knowledge of radar quantity. Simulation results demonstrate that the proposed method achieves superior deinterleaving performance in complex electromagnetic scenarios, outperforming state‐of‐the‐art approaches.