A method for fast identification of unknown radio signals based on a large electromagnetic spectrum model

Abstract

This invention discloses a rapid identification method for unknown radio signals based on a large electromagnetic spectrum model, relating to the field of electromagnetic spectrum monitoring technology. The method involves acquiring clean spectrum samples for self-supervised mask modeling pre-training to construct an electromagnetic spectrum spatiotemporal cognitive model capable of predicting expected spectral patterns based on context. Real-time frequency sweep data is input into the model to obtain the expected spectrum map, and its similarity difference with the actual spectrum map is calculated. Cosine distance is calculated as the feature difference by extracting features from the intermediate layers of the model, and the two are fused to obtain a cognitive difference matrix, which is then mapped to a two-dimensional surprise map. Spatiotemporal correlation tracking is performed on multiple consecutive frames of surprise maps to form surprise trajectories. The type of credible unknown signal is determined based on the frequency of occurrence, the mean surprise level, and frequency stability conditions. Original spectrum segments from the signal occurrence region are collected as positive samples, and the model parameters are incrementally fine-tuned using a low-rank adaptation matrix. This invention enables the discovery of unknown radio signals without relying on prior knowledge.

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