One dimensional detection for radio signals based on SpecUNeSt-MLP

Detecting and precisely localizing multiple radio signals in wideband power spectral density (PSD) vectors under low signal-to-noise ratio (SNR) conditions remains a fundamental challenge for spectrum monitoring and cognitive radio. Traditional energy detection and threshold-based methods suffer from the SNR-wall effect and poor performance in dense, non-cooperative environments. While deep-learning approaches have advanced the field, most treat spectrum sensing as a binary classification task, discarding the fine-grained frequency-bin information needed for accurate signal boundary and parameter estimation. In this paper, we propose SpecUNeSt-MLP, an one-dimensional semantic segmentation framework built upon the U-Net encoder-decoder architecture. The model directly predicts per-frequency-bin occupancy masks from normalized PSD vectors, thereby simultaneously achieving signal detection and parameter estimation (center frequency and bandwidth). Three key enhancements are introduced: (1) a 1D-adapted Split-Attention mechanism in the encoder that dynamically recalibrates cross-channel features to enhance weak-signal extraction under heavy noise; (2) a Bidirectional Rolling MLP (RMLP) module in the bottleneck that efficiently captures long-range spectral dependencies with linear complexity; and (3) Feature Incentive Blocks that refine feature representations through lightweight channel projection. Extensive experiments on 67,200 synthetic broadband PSDs and 820 real-world UAV PSD samples demonstrate the effectiveness of SpecUNeSt-MLP. Across all tested SNR levels (from −4 dB to 8 dB), SpecUNeSt-MLP consistently outperforms LAD, FCN, SS-CNN, and SS-LSTM in terms of detection probability, false alarm rate, and average localization error. Compared with the recent RMP baseline, SpecUNeSt-MLP achieves better performance under low-SNR conditions while maintaining comparable performance at higher SNR levels, with only one-sixth of the parameter count of RMP. These results demonstrate that SpecUNeSt-MLP provides an effective and computationally efficient solution for fine-grained spectrum sensing in complex electromagnetic environments.