Radar Signal Deinterleaving in Complex Electromagnetic Environments by a Multi-Class Classification Perspective

Radar signal deinterleaving is challenging due to dense pulse interleaving and diverse PRI modulations. This work reframes it as a multi-class classification problem, treating each emitter as a distinct class. Existing methods suffer from error accumulation in sequential processing or fail to integrate parallel classifier outputs effectively. To address these flaws, we propose OvR-C-MC, a complete one-vs.-rest (OvR) decomposition framework. Key innovations include (1) true multi-class decomposition: parallel binary classifiers maintain the OvR paradigm's theoretical guarantees, avoiding error propagation in sequential binary classifiers. We integrate classifier outputs with a prioritisation mechanism to resolve conflicts, ensuring a more robust and accurate classification process than existing methods. (2) HMC-based OvR classifier: hidden Markov chains (HMCs) form the basis of each binary classifier, enabling support for any regularised PRI modulation types through state transition property and providing a more comprehensive solution. Experimental results demonstrate that the proposed method significantly outperformed existing approaches, particularly in dense interleaved scenarios, whereas maintaining compatibility with diverse PRI modulation types. Thus, the proposed systematic perspective for radar signal deinterleaving provides robust support for radar signal reconnaissance in complex electromagnetic environments.