Radar Emitter Identification Based on Typical Parameter Sequence: HBNP Clustering, Hierarchical Denoising and LSTM Classification

To address the challenges of ‘high-density interference’ and ‘multi-mode parameter agility’ in Radar Emitter Identification (REI) under complex electromagnetic environments, as well as the limitations of existing models-pulse sequence models have weak anti-noise capability, whereas statistical feature models are prone to multi-mode parameter confusion-this paper proposes an REI method based on Typical Parameter Sequences (TPS). First, a three-level ‘operational mode-beam dwell-pulse group’ signal model is constructed to clarify the hierarchy of key radar features and lay a foundation for both the rationality of TPS and sliding windows design in TPS extraction. A Pulse Repetition Interval (PRI) probability model under pulse interference and loss is also established, providing a theoretical basis for noise suppression. Second, Hierarchical Bayesian Nonparametrics (HBNP) clustering and hierarchical denoising extract local typical parameters, which are concatenated into global TPS (retains temporal information, anti-noise, data compression) via sliding windows. Finally, Long Short-Term Memory (LSTM) realises emitter identification. Simulation experiments show that: In strong noise environments, the proposed model's accuracy is significantly higher than that of pulse sequence models after accumulating a limited number of beam dwells; in multi-mode switching scenarios, its accuracy is much higher than that of statistical feature models, helping alleviate multi-mode parameter confusion.