Accurately detecting the structure of top coal is crucial for achieving intelligent fully-mechanized caving mining. However

the complex structure of coal seams in China

often consisting of multiple layers of separated gangue

can significantly impact the effectiveness of fully-mechanized caving mining. To address this challenge

this paper proposes a precise detection method for top coal structure based on multi-frequency radar fusion. This method aims to improve the detection accuracy of the shallow part within the radar antenna’s detection depth and interpret the structural information inside the top coal. The main research steps are as follows: Firstly

preprocessing and spatial alignment of radar data of different frequencies are carried out to establish the spatial correspondence between radar data of different frequencies. Secondly

a sliding window and wavelet transform weighted fusion method is employed to process the multi-frequency radar data. The time-varying weight value of each frequency signal is determined based on the energy proportion of each segment wavelet signal in the window. Additionally

an edge detection algorithm is introduced to enhance the fusion efficiency of the wavelet transform to the radar data

thereby achieving effective fusion of the multi-frequency radar data. Finally

taking into account the differences in dielectric constants between coal

gangue

and rock

as well as the attenuation characteristics of electromagnetic wave propagation

an internal echo intensity model of top coal is established. The interface information between coal

gangue

and rock inside the top coal is calculated using a stratified identification method

enabling the inverse interpretation of the internal structure of top coal

including the thickness of top coal

the thickness and number of gangue layers

and the spacing between gangue layers. The test results demonstrate that the proposed method can effectively integrate radar data of different frequencies and accurately detect the internal structural characteristics of top coal. Moreover

the detection errors of the thickness of top coal

the thickness of gangue

and the spacing between gangue layers are all less than 10%. This method enables the accurate detection of top coal structure

providing theoretical and technical support for the intelligent fully-mechanized caving mining.