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

ABSTRACT 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.