Leveraging a Repetitive Filtering Framework for Robust Pulse Stream Extraction in Distributed Optical Fiber Sensing Signals

Pulse streams in signals sensing multiple objects exhibit flexible features and complex patterns that pose detection challenges from a statistical perspective. We propose a repetitive filtering framework and establish its fundamental properties. By leveraging the resulting peak pillar stripe-based data structure, our approach controllably captures highly erratic pulse streams, transforming an otherwise unmanageable process into a deterministic framework. Through clustering and analysis of these stripes, we derive stripe intervals. Subsequent splitting and merging operations yield the final pulse stream intervals. This framework serves not as a real-time prediction model constrained by algorithmic time complexity, but as a preparatory stage for generating annotations to facilitate supervised learning. This enables concentrated focus on precision enhancement and extraction of finer signal details. We establish and label a dataset to evaluate our method. Results demonstrate that all pulses, whether weak or strong, are well-represented in the stripe patterns. Experiments confirm precise estimation of labeled pulses across varying ranges and amplitudes, with the majority corresponding to strong, deterministic pulses. Unlabeled weak pulses also exhibit clear reflections in the peak pillar stripes.