As the demand for efficient and automated coal mining continues to rise, accurately identifying the mixing ratio of coal and gangue becomes a crucial step in enhancing the intelligence of the top-coal mining process. Currently, the main challenges stem from the background noise. These factors complicate the effective collection of key signals. Additionally, existing identification methods often rely on single-signal analysis, which limits their accuracy and robustness. To address these issues, this paper presents a novel method for coal-gangue recognition based on key time-frequency information from electromagnetic waves combined with decision fusion. First, a propagation model for electromagnetic waves in coal-gangue mixtures is established. This model reveals the key time-frequency domain information during the propagation process and is subjected to numerical simulation. Subsequently, a coal-gangue identification model is constructed based on the feature extraction of key time-frequency domain information. To further enhance identification accuracy, a decision fusion method based on an improved Analytic Hierarchy Process (IAHP) is designed. Finally, a simulation experimental platform for top-coal mining is built to validate the proposed method. Experimental results indicate that this method achieves a high level of accuracy in coal and gangue identification, providing effective technical support for automated coal mining.
Li et al. (2026) studied this question.