Abstract: The radio frequency front-end is an important part of mine ultra-wideband (UWB) positioning system. Its electromagnetic performance affects positioning precision. At present, the RF front-end design of the UWB positioning system is generally simulated by ADS or HFSS for single device or chip. With the increasing frequency band of RF front-end design, the parasitic effect caused by three-dimensional structures such as discrete components and transmission lines has more and more influence on the performance of RF front-end circuits. It is necessary to study the electromagnetic co-simulation method of the board-level RF front-end. In order to solve the above problems, a mine UWB RF front-end electromagnetic co-simulation method based on ADS and HFSS is proposed. Firstly, the passive device is modeled by HFSS software. The corresponding snp file is obtained by directly simulating with HFSS software. Secondly, ADS software is used to build the schematic diagram of active devices, connect the parameter reading control with the schematic diagram, and import the snp file into the control. Finally, the schematic diagram is simulated in ADS, and the joint operation between ADS and HFSS is realized through S parameters as the medium to realize the joint simulation of UWB RF front-end electromagnetic characteristics. ADS and HFSS are used to co-simulate the active components, passive components and the whole board-level circuit of the UWB RF front-end. The test samples are made according to the simulation principle. The experimental results show that the co-simulation results match the measured results of the samples. It can be used for the design of the UWB RF front-end and the comprehensive test of electromagnetic performance. The RF front-end designed by the electromagnetic co-simulation method is made into a PCB sample and used in a UWB positioning system to test the positioning limit distance. The test results show that the RF front-end designed by the electromagnetic co-simulation method can completely meet the performance requirements of the actual product. It can accurately predict the effect of the actual product in the design stage, improve the design efficiency and reduce the design cost.